[ { "id": "kovacs-2026-sbg", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/226679", "title": "Style Brush: Guided Style Transfer for 3D Objects", "date": "2026-02-16", "abstract": "We introduce Style Brush, a novel style transfer method for textured meshes designed to empower artists with fine-grained control over the stylization process. Our approach extends traditional 3D style transfer methods by introducing a novel loss function that captures style directionality, supports multiple style images or portions thereof and enables smooth transitions between styles in the synthesized texture. The use of easily generated guiding textures streamlines user interaction, making our approach accessible to a broad audience. Extensive evaluations with various meshes, style images and contour shapes demonstrate the flexibility of our method and showcase the visual appeal of the generated textures. Finally, the results of a user study indicate that our approach generates visually appealing mesh textures that adhere to user-defined guidance and enable users to retain creative control during stylization. Our implementation is available on: https://github.com/AronKovacs/style-brush.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1950, 5415, 1410 ], "articleno": "e70308", "doi": "10.1111/cgf.70308", "issn": "1467-8659", "journal": "Computer Graphics Forum", "pages": "18", "publisher": "WILEY", "research_areas": [], "keywords": [ "3D style transfer", "directional guidance", "mesh texture synthesis", "user guidance" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2026/kovacs-2026-sbg/", "__class": "Publication" }, { "id": "chaves-de-plaza-2026-logcc", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/226667", "title": "LoGCC: Local-to-Global Correlation Clustering for Scalar Field Ensembles", "date": "2026-02", "abstract": "Correlation clustering (CC) offers an effective approach to analyze scalar field ensembles by detecting correlated regions and consistent structures, enabling the extraction of meaningful patterns. However, existing CC methods are computationally expensive, making them impractical for both interactive analysis and large-scale scalar fields. We introduce the Local-to-Global Correlation Clustering (LoGCC) framework, which accelerates pivot-based CC by leveraging the spatial structure of scalar fields and the weak transitivity of correlation. LoGCC operates in two stages: a local step that uses the neighborhood graph of the scalar field's spatial domain to build highly correlated local clusters, and a global step that merges them into global clusters. We implement the LoGCC framework for two well-known pivot-based CC methods, Pivot and CN-Pivot, demonstrating its generality. Our evaluation using synthetic and real-world meteorological and medical image segmentation datasets shows that LoGCC achieves speedups—up to 15 × for Pivot and 200 × for CN-Pivot—and improved scalability to larger scalar fields, while maintaining cluster quality. These contributions broaden the applicability of correlation clustering in large-scale and interactive analysis settings.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5565, 1410, 5566, 5567, 5568, 5569, 5570 ], "doi": "10.1109/TVCG.2025.3630550", "issn": "1941-0506", "journal": "IEEE Transactions on Visualization and Computer Graphics", "number": "2", "pages": "12", "pages_from": "2260", "pages_to": "2271", "publisher": "IEEE COMPUTER SOC", "volume": "32", "research_areas": [], "keywords": [ "Correlation Clustering", "Clustering Algorithms", "Uncertainty Visualization", "Ensemble Visualization", "Scalar Field Ensemble Analysis" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2026/chaves-de-plaza-2026-logcc/", "__class": "Publication" }, { "id": "oda-2025-artevoviewer", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/225534", "title": "ArtEvoViewer: A System for Visualizing Interpersonal Influence Among Painters", "date": "2025-10-31", "abstract": "Large-scale and objective painting analyses have recently gained attention. In particular, analyzing influence between individual painters requires substantial effort and is hard to reproduce due to subjectivity. Despite increasing demand for automatic estimation, this remains unresolved because such influence is complex and often directional, making it difficult to model. In this paper, we develop an interactive system that visualizes, manipulates, and analyses chains of painterly influence as a network. Using 32,401 paintings, the system infers directional links from color and brushstroke features. The resulting network based on color style features captures stylistic lineages such as landscape-focused and portrait-focused streams, while a multifaceted analysis of Picasso shows that Cézanne's impact appears in brushwork rather than color. Our contributions are twofold: (1) the use of an evolutionary model to assign explicit direction to painter influence and support art historical interpretation, and (2) providing a visualization system that allows dynamic comparison of influence networks based on multiple image features.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5544, 5545, 1813, 1850, 166, 1410, 1754 ], "booktitle": "2025 29th International Conference Information Visualisation (IV)", "date_from": "2025-08-05", "date_to": "2025-08-08", "doi": "10.1109/IV68685.2025.00041", "event": "29th International Conference Information Visualisation (IV)", "isbn": "979-8-3315-7741-4", "lecturer": [ 5544 ], "location": "Darmstadt", "pages": "6", "pages_from": "171", "pages_to": "176", "publisher": "IEEE", "research_areas": [], "keywords": [ "artist influence estimation", "cultural evolution", "digital humanities", "paintings", "system", "visualization" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/oda-2025-artevoviewer/", "__class": "Publication" }, { "id": "ehlers-2025-www", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/217489", "title": "Wiggle! Wiggle! Wiggle! Visualizing uncertainty in node attributes in straight-line node-link diagrams using animated wiggliness", "date": "2025-10", "abstract": "Uncertainty is common to most types of data, from meteorology to the biomedical sciences. Here, we are interested in the visualization of uncertainty within the context of multivariate graphs, specifically the visualization of uncertainty attached to node attributes. Many visual channels offer themselves up for the visualization of node attributes and their uncertainty. One controversial and relatively under-explored channel, however, is animation, despite its conceptual advantages. In this paper, we investigate node “wiggliness”, i.e. uncertainty-dependent pseudo-random motion of nodes, as a potential new visual channel with which to communicate node attribute uncertainty. To study wiggliness’ effectiveness, we compare it against three other visual channels identified from a thorough review of uncertainty visualization literature—namely node enclosure, node fuzziness, and node color saturation. In a larger-scale, mixed method, Prolific-crowd-sourced, online user study of 160 participants, we quantitatively and qualitatively compare these four uncertainty encodings across eight low-level graph analysis tasks that probe participants’ abilities to parse the presented networks both on an attribute and topological level. We ultimately conclude that all four uncertainty encodings appear comparably useful—as opposed to previous findings. Wiggliness may be a suitable and effective visual channel with which to communicate node attribute uncertainty, at least for the kinds of data and tasks considered in our study.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1850, 1813, 5488, 5417, 1464, 1410 ], "articleno": "104290", "doi": "10.1016/j.cag.2025.104290", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "131", "research_areas": [], "keywords": [ "Network Visualization", "Uncertainty Visualization", "Animation", "Fuzziness", "Enclosure", "Saturation" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/ehlers-2025-www/", "__class": "Publication" }, { "id": "casares-magaz-2025-rog", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/221525", "title": "Risk of genitourinary late effects after radiotherapy for prostate cancer associated with early changes in bladder shape", "date": "2025-10", "abstract": "Background and purpose: The risk of genitourinary late effects is a major dose-limiting factor in radiotherapy for prostate cancer. By using shape analysis and machine learning, the aim of this study was to evaluate whether bladder shape descriptors from the first week of treatment could identify patients experiencing genitourinary late effects.\r\nMaterial and methods: From a cohort of 258 prostate cancer patients treated with daily cone-beam computed tomography (CBCT)-guided radiotherapy (prescription doses of 77.4–81.0 Gy), 7 pre-treatment asymptomatic cases experiencing RTOG genitourinary late effects ≥Grade 2 and 21 matched controls were selected. The bladder was manually contoured on each CBCT, and a 17-D vector comprising shape descriptors was used for patient clustering, focusing on bladder contours from the first week of treatment. ANOVA was used to test statistical significance of descriptors across and within clusters.\r\nResults: Of the contours from the first week of treatment, 84 % could be classified in two main clusters with distinct bladder shape characteristics. This cluster stratification remained identical when bladder contours from the entire course of treatment were used. Convexity, elliptic variance and compactness were significantly different between patients with vs. without genitourinary late effects ≥Grade 2 (p < 0.05). Dice Coefficients between predictive models using descriptors of the first week and the voxels’ probability of belonging to the bladder were above 93 ± 6 % (median ± interquartile range).\r\nConclusion: Bladder shape descriptors in the first week of treatment showed potential to predict the risk of developing genitourinary late effects after radiotherapy for prostate cancer.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1444, 1410, 5406, 5505, 1568, 5506, 5507, 5508, 5509 ], "articleno": "100855", "doi": "10.1016/j.phro.2025.100855", "issn": "2405-6316", "journal": "Physics and Imaging in Radiation Oncology", "open_access": "yes", "pages": "5", "publisher": "Elsevier Inc.", "volume": "36", "research_areas": [], "keywords": [ "Adaptive radiotherapy", "Machine learning", "Prostate cancer radiotherapy", "Radiation-induced late effects" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "casares-magaz-2025-rog-paper.pdf", "type": "application/pdf", "size": 3013928, "path": "Publication:casares-magaz-2025-rog", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/casares-magaz-2025-rog/casares-magaz-2025-rog-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/casares-magaz-2025-rog/casares-magaz-2025-rog-paper:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/casares-magaz-2025-rog/", "__class": "Publication" }, { "id": "kummer-2025-fvo", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/219860", "title": "Flattening-based visualization of supine breast MRI", "date": "2025-09-22", "abstract": "We propose two novel visualization methods optimized for supine breast images that “flatten” breast tissue, facilitating examination of larger tissue areas within each coronal slice. Breast cancer is the most frequently diagnosed cancer in women, and early lesion detection is crucial for reducing mortality. Supine breast magnetic resonance imaging (MRI) enables better lesion localization for image-guided interventions; however, traditional axial visualization is suboptimal because the tissue spreads over the chest wall, resulting in numerous fragmented slices that radiologists must scroll through during standard interpretation. Using a human-centered design approach, we incorporated user and expert feedback throughout the co-design and evaluation stages of our flattening methods. Our first proposed method, a surface-cutting approach, generates offset surfaces and flattens them independently using As-Rigid-As-Possible (ARAP) surface mesh parameterization. The second method uses a landmark-based warp to flatten the entire breast volume at once. Expert evaluations revealed that the surface-cutting method provides intuitive overviews and clear vascular detail, with low metric (2–2.5%) and area (3.7–4.4%) distortions. However, independent slice flattening can introduce depth distortions across layers. The landmark warp offers consistent slice alignment and supports direct annotations and measurements, with radiologists favoring it for its anatomical accuracy. Both methods significantly reduced the number of slices needed to review, highlighting their potential for time savings and clinical impact — an essential factor for adopting supine MRI.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5500, 5501, 5502, 1410, 231 ], "ac_number": "AC17670018", "articleno": "104395", "doi": "10.1016/j.cag.2025.104395", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "open_access": "yes", "pages": "9", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "133", "research_areas": [], "keywords": [ "Image reformation", "Medical visualization", "Breast imaging", "Radiology" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "kummer-2025-fvo-paper.pdf", "type": "application/pdf", "size": 3063948, "path": "Publication:kummer-2025-fvo", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/kummer-2025-fvo/kummer-2025-fvo-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/kummer-2025-fvo/kummer-2025-fvo-paper:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/kummer-2025-fvo/", "__class": "Publication" }, { "id": "musleh-2025-trustme", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/215604", "title": "TrustME: A Context-Aware Explainability Model to Promote User Trust in Guidance", "date": "2025-04-21", "abstract": "Guidance-enhanced approaches are used to support users in making sense of their data and overcoming challenging analytical scenarios. While recent literature underscores the value of guidance, a lack of clear explanations to motivate system interventions may still negatively impact guidance effectiveness. Hence, guidance-enhanced VA approaches require meticulous design, demanding contextual adjustments for developing appropriate explanations. Our paper discusses the concept of explainable guidance and how it impacts the user-system relationship-specifically, a user's trust in guidance within the VA process. We subsequently propose a model that supports the design of explainability strategies for guidance in VA. The model builds upon flourishing literature in explainable AI, available guidelines for developing effective guidance in VA systems, and accrued knowledge on user-system trust dynamics. Our model responds to challenges concerning guidance adoption and context-effectiveness by fostering trust through appropriately designed explanations. To demonstrate the model's value, we employ it in designing explanations within two existing VA scenarios. We also describe a design walk-through with a guidance expert to showcase how our model supports designers in clarifying the rationale behind system interventions and designing explainable guidance.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 703, "image_height": 706, "name": "musleh-2025-trustme-.png", "type": "image/png", "size": 75894, "path": "Publication:musleh-2025-trustme", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/musleh-2025-trustme-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/musleh-2025-trustme-:thumb{{size}}.png" }, "sync_repositum_override": "event,number,pages_from,pages_to,volume", "repositum_presentation_id": null, "authors": [ 1867, 1410, 5418 ], "doi": "10.1109/TVCG.2025.3562929", "event": "IEEE Vis 2025", "issn": "1941-0506", "journal": "IEEE Transactions on Visualization and Computer Graphics", "number": "10", "open_access": "yes", "pages": "17", "pages_from": "8040", "pages_to": "8056", "publisher": "IEEE COMPUTER SOC", "volume": "31", "research_areas": [ "InfoVis" ], "keywords": [ "Explainability", "Explainable Guidance", "User Trust", "Visual Analytics" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 703, "image_height": 706, "name": "musleh-2025-trustme-.png", "type": "image/png", "size": 75894, "path": "Publication:musleh-2025-trustme", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/musleh-2025-trustme-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/musleh-2025-trustme-:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/", "__class": "Publication" }, { "id": "peischl-2025-ipo", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/213937", "title": "Investigating the Propagation of CT Acquisition Artifacts along the Medical Imaging Pipeline", "date": "2025-02", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5454, 1410 ], "booktitle": "Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 1)", "date_from": "2025-02-26", "date_to": "2025-03-28", "doi": "10.5220/0013254200003912", "event": "IVAPP 2025 - Part of VISIGRAPP, the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.", "isbn": "978-989-758-728-3", "lecturer": [ 5454 ], "location": "Porto", "pages": "13", "pages_from": "752", "pages_to": "764", "research_areas": [], "keywords": [ "Biomedical Visualization and Applications", "Uncertainty Visualization", "Visual Analytics" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/peischl-2025-ipo/", "__class": "Publication" }, { "id": "musleh-2024-conan", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/205807", "title": "ConAn: Measuring and Evaluating User Confidence in Visual Data Analysis Under Uncertainty", "date": "2025-02", "abstract": "User confidence plays an important role in guided visual data analysis scenarios, especially when uncertainty is involved in the analytical process. However, measuring confidence in practical scenarios remains an open challenge, as previous work relies primarily on self-reporting methods. In this work, we propose a quantitative approach to measure user confidence—as opposed to trust—in an analytical scenario. We do so by exploiting the respective user interaction provenance graph and examining the impact of guidance using a set of network metrics. We assess the usefulness of our proposed metrics through a user study that correlates results obtained from self-reported confidence assessments and our metrics—both with and without guidance. The results suggest that our metrics improve the evaluation of user confidence compared to available approaches. In particular, we found a correlation between self-reported confidence and some of the proposed provenance network metrics. The quantitative results, though, do not show a statistically significant impact of the guidance on user confidence. An additional descriptive analysis suggests that guidance could impact users' confidence and that the qualitative analysis of the provenance network topology can provide a comprehensive view of changes in user confidence. Our results indicate that our proposed metrics and the provenance network graph representation support the evaluation of user confidence and, subsequently, the effective development of guidance in VA.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 8170, "image_height": 5961, "name": "musleh-2024-conan-.png", "type": "image/png", "size": 9874701, "path": "Publication:musleh-2024-conan", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/musleh-2024-conan-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/musleh-2024-conan-:thumb{{size}}.png" }, "sync_repositum_override": "date,projects,articleno,event,number,volume", "repositum_presentation_id": null, "authors": [ 1867, 5418, 1850, 1410 ], "articleno": "e15272", "doi": "10.1111/cgf.15272", "event": "EuroVis 2025", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "1", "open_access": "yes", "pages": "18", "publisher": "WILEY", "volume": "44", "research_areas": [], "keywords": [ "decision making", "uncertainty", "user confidence", "visual analytics", "guided visual data analysis" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 8170, "image_height": 5961, "name": "musleh-2024-conan-.png", "type": "image/png", "size": 9874701, "path": "Publication:musleh-2024-conan", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/musleh-2024-conan-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/musleh-2024-conan-:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/", "__class": "Publication" }, { "id": "amabili-2025-lpb", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/216591", "title": "Leveraging Popular Board Games to Teach Data Visualization", "date": "2025", "abstract": "To address the challenges in visualization education—particularly in motivating and engaging students—we propose the conceptual adaptation of popular board games into educational data visualization games. We present five unique adaptations ofwell-known board games, integrating their mechanics and materials into a data visualization learning process. For each game, we outline specific learning objectives and suggest strategies to extend the game-based approach to broader data visualization education. By combining familiar, engaging game mechanics with visualization content, we aim to foster critical engagement with the learning material while providing students with a foundational understanding of data visualization concepts. Early qualitative results from one of the games indicate a positive impact on players’ learning, boosting engagement and enjoyment.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1861, 166, 1410 ], "booktitle": "Visgames 2025: EuroVis Workshop on Visualization Play, Games, and Activities", "date_from": "2025-06-02", "date_to": "2025-06-06", "doi": "10.2312/visgames.20251165", "editor": "Stoiber, C. and Boucher, M. and de Jesus Oliveira, V. A. and Schetinger, V. and Filipov, V. and Raidou, R. G. and Amabili, L. and Keck, M.", "event": "VisGames 2025 colocated with EuroVis 2025 - 27th EG Conference on Visualization", "lecturer": [ 1861 ], "location": "Luxembourg City", "open_access": "yes", "pages": "6", "publisher": "The Eurographics Association", "research_areas": [], "keywords": [ "Human-centered computing", "visualization theory", "concepts and paradigms", "Information visualization" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "amabili-2025-lpb-paper.pdf", "type": "application/pdf", "size": 525621, "path": "Publication:amabili-2025-lpb", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/amabili-2025-lpb/amabili-2025-lpb-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/amabili-2025-lpb/amabili-2025-lpb-paper:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/amabili-2025-lpb/", "__class": "Publication" }, { "id": "ehlers-2025-penta", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/216534", "title": "Penta: Towards Visualizing Compound Graphs as Set-Typed Data", "date": "2025", "abstract": "Compound graphs are graphs whose nodes, in addition to topological connections, share group-level relationships. The need to incorporate both topological and group-level relationships makes them inherently challenging to visualize, especially for large data. We present Penta, a prototypical dashboard that, by combining elements of compound graph and set visualization, provides a complete view of both types of relationships. To this end, we employ five linked views that provide insight into a compound graph’s i) global and set local topology using both hypernode and traditional node-link diagrams, respectively, ii) set and entity-level relationship and identity using similarity matrices linked by a bipartite node-link diagram, as well as iii) node-centric topology across sets visualized as a layered node-link diagram. We demonstrate the workflow and advantages of Penta in three small-scale case studies, using character co-occurrence networks as well as biochemical pathway data. While still a prototype, the proposed dashboard shows promise in facilitating a complete visual exploration of the topology and group-level relationships present in compound graphs, simultaneously.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1850, 5479, 1410 ], "booktitle": "Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1 GRAPP, HUCAPP and IVAPP: IVAPP,", "date_from": "2025-02-26", "date_to": "2025-02-28", "doi": "10.5220/0013242300003912", "event": "20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP , GRAPP, HUCAPP and IVAPP 2025)", "isbn": "978-989-758-728-3", "lecturer": [ 5479 ], "location": "Porto", "pages": "9", "pages_from": "913", "pages_to": "921", "volume": "1", "research_areas": [], "keywords": [ "Compound Graph", "Ego Network", "Network Visualization", "Set Visualization" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/ehlers-2025-penta/", "__class": "Publication" }, { "id": "pahr-2025-nodkant", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/216536", "title": "NODKANT: exploring constructive network physicalization", "date": "2025", "abstract": "Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit—NODKANT—for constructing modular node-link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed-methods between-subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low-level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step-by-step instructions to create a predefined layout, and the third group received a pre-constructed representation. While free construction proved on average more time-consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step-by-step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10–14 days compared to those who were given a pre-constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/.", "authors_et_al": false, "substitute": null, "main_image": { "description": "An IKEA-like illustration of a person looking at a dataset, assembling a graph manually, and being happy with the result", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1280, "image_height": 536, "name": "pahr-2025-nodkant-teaser.jpg", "type": "image/jpeg", "size": 46516, "path": "Publication:pahr-2025-nodkant", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/pahr-2025-nodkant/pahr-2025-nodkant-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/pahr-2025-nodkant/pahr-2025-nodkant-teaser:thumb{{size}}.png" }, "sync_repositum_override": "authors,projects", "repositum_presentation_id": null, "authors": [ 1813, 5488, 1850, 5417, 5489, 1059, 1464, 1410 ], "ac_number": "AC17571629", "articleno": "e70140", "doi": "10.1111/cgf.70140", "issn": "1467-8659", "journal": "Computer Graphics Forum", "open_access": "yes", "pages": "12", "publisher": "WILEY", "research_areas": [], "keywords": [ "CCS Concepts", "Empirical studies in visualization", "human-centered computing", "Visualization Application Domains" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "pahr-2025-nodkant-paper.pdf", "type": "application/pdf", "size": 15020616, "path": "Publication:pahr-2025-nodkant", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/pahr-2025-nodkant/pahr-2025-nodkant-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/pahr-2025-nodkant/pahr-2025-nodkant-paper:thumb{{size}}.png" }, { "description": "An IKEA-like illustration of a person looking at a dataset, assembling a graph manually, and being happy with the result", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1280, "image_height": 536, "name": "pahr-2025-nodkant-teaser.jpg", "type": "image/jpeg", "size": 46516, "path": "Publication:pahr-2025-nodkant", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/pahr-2025-nodkant/pahr-2025-nodkant-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/pahr-2025-nodkant/pahr-2025-nodkant-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/pahr-2025-nodkant/", "__class": "Publication" }, { "id": "ehlers-2024-mmm", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/205726", "title": "Me! Me! Me! Me! A study and comparison of ego network representations", "date": "2024-12", "abstract": "From social networks to brain connectivity, ego networks are a simple yet powerful approach to visualizing parts of a larger graph, i.e. those related to a selected focal node — the so-called “ego”. While surveys and comparisons of general graph visualization approaches exist in the literature, we note (i) the many conflicting results of comparisons of adjacency matrices and node-link diagrams, thus motivating further study, as well as (ii) the absence of such systematic comparisons for ego networks specifically. In this paper, we propose the development of empirical recommendations for ego network visualization strategies. First, we survey the literature across application domains and collect examples of network visualizations to identify the most common visual encodings, namely straight-line, radial, and layered node-link diagrams, as well as adjacency matrices. These representations are then applied to a representative, intermediate-sized network and subsequently compared in a large-scale, crowd-sourced user study in a mixed-methods analysis setup to investigate their impact on both user experience and performance. Within the limits of this study, and contrary to previous comparative investigations of adjacency matrices and node-link diagrams (outside of ego networks specifically), participants performed systematically worse when using adjacency matrices than those using node-link diagrammatic representations. Similar to previous comparisons of different node-link diagrams, we do not detect any notable differences in participant performance between the three node-link diagrams. Lastly, our quantitative and qualitative results indicate that participants found adjacency matrices harder to learn, use, and understand than node-link diagrams. We conclude that in terms of both participant experience and performance, a layered node-link diagrammatic representation appears to be the most preferable for ego network visualization purposes.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1850, 1813, 5417, 1464, 1410 ], "articleno": "104123", "doi": "10.1016/j.cag.2024.104123", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "open_access": "yes", "pages": "15", "pages_from": "1", "pages_to": "15", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "125", "research_areas": [ "NetVis" ], "keywords": [ "Adjacency matrix", "Ego network visualization", "Layered node-link diagram", "Radial node-link diagram", "Straight-line node-link diagram", "User study" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ehlers-2024-mmm-paper.pdf", "type": "application/pdf", "size": 2475868, "path": "Publication:ehlers-2024-mmm", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-mmm/ehlers-2024-mmm-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-mmm/ehlers-2024-mmm-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-mmm/", "__class": "Publication" }, { "id": "pahr-2024-squishicalization", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/208691", "title": "Squishicalization: Exploring Elastic Volume Physicalization", "date": "2024-12", "abstract": "We introduce Squishicalization , a pipeline for generating physicalizations of volumetric data that encode scalar information through their physical characteristics—specifically, by varying their “squishiness” or local elasticity. Data physicalization research is increasingly exploring multisensory information encoding, with a particular focus on enhancing direct interactivity. With Squishicalization , we leverage the tactile dimension of physicalization as a means of direct interactivity. Inspired by conventional volume rendering, we adapt the concept of transfer functions to encode scalar values from volumetric data into local elasticity levels. In this way, volumetric scalar data are transformed into sculptures, where the elasticity represents physical properties such as the material's density distribution within the volume. In our pipeline, scalar values guide the weighted sampling of the scalar field. The sampled data is then processed through Voronoi tessellation to create a sponge-like structure, which can be printed with consumer-grade 3D printers and readily available filament. To validate our pipeline, we conduct a computational and mechanical evaluation, as well as a two-stage perceptual study of the capabilities of our generated squishicalizations. To further investigate potential application scenarios, we interview experts across several domains. Finally, we summarize actionable insights and future avenues for the application of our All supplemental materials are available at https://osf.io/35gnv/?view_only=605e5085061f40439a98545f0c447cf3 .", "authors_et_al": false, "substitute": null, "main_image": { "description": "A hand coming from the left side of the picture squeezes the face of a printed representation of an MRI.", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2177, "image_height": 1639, "name": "pahr-2024-squishicalization-teaser.png", "type": "image/png", "size": 3380920, "path": "Publication:pahr-2024-squishicalization", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-squishicalization/pahr-2024-squishicalization-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-squishicalization/pahr-2024-squishicalization-teaser:thumb{{size}}.png" }, "sync_repositum_override": "projects,open_access", "repositum_presentation_id": null, "authors": [ 1813, 5430, 1464, 1410 ], "doi": "10.1109/TVCG.2024.3516481", "issn": "1941-0506", "journal": "IEEE Transactions on Visualization and Computer Graphics", "open_access": "yes", "pages": "14", "pages_from": "1", "pages_to": "14", "publisher": "IEEE COMPUTER SOC", "research_areas": [ "Fabrication" ], "keywords": [ "Elasticity", "Three Dimensional Printing", "Pipelines", "Fabrication", "Microstructures", "Rendering Computer Graphics", "Encoding", "Printing", "Data Physicalization", "Data Visualization", "Digital Fabrication" ], "weblinks": [], "files": [ { "description": "A hand coming from the left side of the picture squeezes the face of a printed representation of an MRI.", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2177, "image_height": 1639, "name": "pahr-2024-squishicalization-teaser.png", "type": "image/png", "size": 3380920, "path": "Publication:pahr-2024-squishicalization", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-squishicalization/pahr-2024-squishicalization-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-squishicalization/pahr-2024-squishicalization-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-squishicalization/", "__class": "Publication" }, { "id": "lucio-2024-yfy", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/208563", "title": "Your Face, Your Anatomy: Flashcard Lenses Enriched with Knowledge Maps for Anatomy Education", "date": "2024-11", "abstract": "Traditional anatomy flashcards, with their recognizable static illustrations on the front side and comprehensive lists of concepts on the back, are a long-standing tool for memorizing and refreshing anatomical concepts. This study repurposes such established tool by introducing two key elements: (i) Augmented Reality (AR) lenses acting as magic mirrors enabling users to view anatomical illustrations mapped onto their own faces, and (ii) a knowledge map layer acting as the card’s backside to visually and explicitly illustrate conceptual connections between anatomical reference points. Using Snapchat’s Lens Studio, we crafted a deck of interactive facial anatomy flashcards to assess the potential of AR and knowledge maps for retaining and refreshing anatomical concepts. We conducted a user study involving 44 university-level students. Divided into two groups, participants utilized either flashcard lenses with knowledge maps or traditional flashcards to quickly grasp and refresh anatomical concepts. By employing an approach that integrates anatomical quizzes for objective assessment with surveys and interviews for subjective feedback, our results indicate that anatomy flashcard lenses with knowledge maps offer a more engaging educational experience, yielding higher user preferences and satisfaction levels compared to traditional flashcards. While both approaches showed similar effectiveness in quiz scores, anatomy flashcard lenses with knowledge maps were favored for their usability, significantly reducing temporal demand. These findings underscore the engaging and effective nature of anatomy flashcard lenses with knowledge maps, highlighting them as an alternative tool for the quick retention and review of anatomical concepts.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5407, 1410, 5412, 5413 ], "booktitle": "Proceedings 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)", "date_from": "2024-10-21", "date_to": "2024-10-25", "editor": "Eck, Ulrich and Sra, Misha and Stefanucci, Jeanine and Sugimoto, Maki and Tatzgern, Markus and Williams, Ian", "event": "2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)", "isbn": "979-8-3315-1647-5", "lecturer": [ 5407 ], "location": "Seattle", "pages": "10", "pages_from": "495", "pages_to": "504", "research_areas": [], "keywords": [ "flashcards", "anatomy education", "mobile augmented reality", "embodied learning", "knowledge maps", "snapchat" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/lucio-2024-yfy/", "__class": "Publication" }, { "id": "furmanova-2024-bvp", "type_id": "book", "tu_id": null, "repositum_id": "20.500.12708/204500", "title": "BioMedical Visualization : Past Work, Current Trends, and Open Challenges", "date": "2024-10-01", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5406, 1248, 1819, 166, 563, 1410 ], "doi": "10.1007/978-3-031-66789-3", "isbn": "978-3-031-66788-6", "pages": "159", "publisher": "Springer Nature", "research_areas": [], "keywords": [ "biomedical visualization" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/furmanova-2024-bvp/", "__class": "Publication" }, { "id": "shilo-2024-vnt", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/200043", "title": "Visual narratives to edutain against misleading visualizations in healthcare", "date": "2024-10", "abstract": "We propose an interactive game based on visual narratives to edutain, i.e., to educate while entertaining, broad audiences against misleading visualizations in healthcare. Uncertainty at various stages of the visualization pipeline may give rise to misleading visual representations. These comprise misleading elements that may negatively impact the audiences by contributing to misinformed decisions, delayed treatments, and a lack of trust in medical information. We investigate whether visual narratives within the setting of an educational game support recognizing and addressing misleading elements in healthcare-related visualizations. Our methodological approach focuses on three key aspects: (i) identifying uncertainty types in the visualization pipeline which could serve as the origin of misleading elements, (ii) designing fictional visual narratives that comprise several misleading elements linking to these uncertainties, and (iii) proposing an interactive game that aids the communication of these misleading visualization elements to broad audiences. The game features eight fictional visual narratives built around misleading visualizations, each with specific assumptions linked to uncertainties. Players assess the correctness of these assumptions to earn points and rewards. In case of incorrect assessments, interactive explanations are provided to enhance understanding For an initial assessment of our game, we conducted a user study with 21 participants. Our study indicates that when participants incorrectly assess assumptions, they also spend more time elaborating on the reasons for their mistakes, indicating a willingness to learn more. The study also provided positive indications on game aspects such as memorability, reinforcement, and engagement, while it gave us pointers for future improvement.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5288, 1410 ], "articleno": "104011", "doi": "10.1016/j.cag.2024.104011", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "pages": "11", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "123", "research_areas": [], "keywords": [ "Healthcare edutainment", "Interactive game", "Misleading visualizations", "Uncertainty", "Visual narratives" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/shilo-2024-vnt/", "__class": "Publication" }, { "id": "pahr-2024-ieo", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/199161", "title": "Investigating the Effect of Operation Mode and Manifestation on Physicalizations of Dynamic Processes", "date": "2024-06", "abstract": "We conducted a study to systematically investigate the communication of complex dynamic processes along a two-dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic). We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism where a liquid is pumped into a draining vessel, with complications illustrated through modifications to the model. The results of a mixed-methods lab study with 28 participants show that both physical manifestation and manual operation have a strong positive impact on the audience's engagement. The study does not show a measurable knowledge increase with respect to cardiovascular pathologies using manually operated physical representations. However, subjectively, participants report a better understanding of the process—mainly through non-visual cues like haptics, but also auditory cues. The study also indicates an increased task load when interacting with the process, which, however, seems to play a minor role for the participants. Overall, the study shows a clear potential of physicalization for the communication of complex dynamic processes, which only fully unfold if observers have to chance to interact with the process.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2634, "image_height": 1232, "name": "pahr-2024-ieo-teaser.png", "type": "image/png", "size": 172079, "path": "Publication:pahr-2024-ieo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-ieo/pahr-2024-ieo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-ieo/pahr-2024-ieo-teaser:thumb{{size}}.png" }, "sync_repositum_override": "projects,date_from,date_to,event,lecturer,pages_from,pages_to", "repositum_presentation_id": null, "authors": [ 1813, 1850, 1464, 1110, 1410 ], "articleno": "e15106", "date_from": "2024-06-27", "date_to": "2024-06-31", "doi": "10.1111/cgf.15106", "event": "EUROVIS", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1813 ], "number": "3", "pages": "12", "pages_from": "1", "pages_to": "12", "publisher": "WILEY", "volume": "43", "research_areas": [ "InfoVis", "MedVis", "Perception" ], "keywords": [ "Data Physicalization", "Study", "Cardiovascular Diseases", "Edutainment", "Human Computer Interaction (HCI)", "Mixed Methods" ], "weblinks": [], "files": [ { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2634, "image_height": 1232, "name": "pahr-2024-ieo-teaser.png", "type": "image/png", "size": 172079, "path": "Publication:pahr-2024-ieo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-ieo/pahr-2024-ieo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-ieo/pahr-2024-ieo-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-ieo/", "__class": "Publication" }, { "id": "kovacs-2024-smt", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/200040", "title": "Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs", "date": "2024-05", "abstract": "Mesh texture synthesis is a key component in the automatic generation of 3D content. Existing learning-based methods have drawbacks—either by disregarding the shape manifold during texture generation or by requiring a large number of different views to mitigate occlusion-related inconsistencies. In this paper, we present a novel surface-aware approach for mesh texture synthesis that overcomes these drawbacks by leveraging the pre-trained weights of 2D Convolutional Neural Networks (CNNs) with the same architecture, but with convolutions designed for 3D meshes. Our proposed network keeps track of the oriented patches surrounding each texel, enabling seamless texture synthesis and retaining local similarity to classical 2D convolutions with square kernels. Our approach allows us to synthesize textures that account for the geometric content of mesh surfaces, eliminating discontinuities and achieving comparable quality to 2D image synthesis algorithms. We compare our approach with state-of-the-art methods where, through qualitative and quantitative evaluations, we demonstrate that our approach is more effective for a variety of meshes and styles, while also producing visually appealing and consistent textures on meshes.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1950, 1919, 1410 ], "articleno": "e15016", "doi": "10.1111/cgf.15016", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "2", "pages": "13", "publisher": "WILEY", "volume": "43", "research_areas": [], "keywords": [ "Deep learning (DL)", "Computer Graphics", "Texture Synthesis" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/kovacs-2024-smt/", "__class": "Publication" }, { "id": "ehlers-2024-vgs", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/196069", "title": "Visualizing Group Structure in Compound Graphs: The Current State, Lessons Learned, and Outstanding Opportunities", "date": "2024-03-11", "abstract": "Compound graphs are common across domains, from social science to biochemical pathway studies, and their visualization is important to both their exploration and analysis. However, effectively visualizing a compound graph's topology and group structure requires careful consideration, as evident by the many different approaches to this particular problem. To better understand the current advancements in compound graph visualization, we have consolidated and streamlined existing surveys' taxonomies. More specifically, we aim to disentangle the visual relationship between graph topology and group structure from the visual encoding used to visualize its group structure in order to identify interesting gaps in the literature. In so doing, we are able to enumerate a number of lessons learned and gain a better understanding of the outstanding research opportunities and practical implications across domains.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1850, 1848, 1464, 1410 ], "booktitle": "Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1, HUCAPP and IVAPP", "date_from": "2024-02-27", "date_to": "2024-02-29", "doi": "10.5220/0012431200003660", "event": "19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications", "isbn": "978-989-758-679-8", "lecturer": [ 1850 ], "location": "Rom", "pages": "12", "pages_from": "697", "pages_to": "708", "research_areas": [ "InfoVis", "NetVis" ], "keywords": [ "compound graph visualization", "literature survey", "group structure visualization" ], "weblinks": [], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-vgs/", "__class": "Publication" }, { "id": "bayat-2024-awt", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/197494", "title": "A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation", "date": "2024-01", "abstract": "We introduce a workflow for the visual assessment of interobserver variability in medical image segmentation. Image segmentation is a crucial step in the diagnosis, prognosis, and treatment of many diseases. Despite the advancements in autosegmentation, clinical practice widely relies on manual delineations performed by radiologists. Our work focuses on designing a solution for understanding the radiologists' thought processes during segmentation and for unveiling reasons that lead to interobserver variability. To this end, we propose a visual analysis tool connecting multiple radiologists' delineation processes with their outcomes, and we demonstrate its potential in a case study.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1693, 1110, 1410 ], "doi": "10.1109/MCG.2023.3333475", "issn": "1558-1756", "journal": "IEEE Computer Graphics and Applications", "number": "1", "pages": "9", "pages_from": "86", "pages_to": "94", "publisher": "IEEE COMPUTER SOC", "volume": "44", "research_areas": [], "keywords": [ "Humans", "Observer Variation", "Workflow", "Algorithms" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/bayat-2024-awt/", "__class": "Publication" }, { "id": "amabili-2024-smg", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/200899", "title": "Show Me the GIFference! Using data-GIFs as Educational Tools", "date": "2024", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1861, 166, 1410 ], "booktitle": "Computer Science Research Notes: CSRN 3401: WSCG 2024: Proceedings", "date_from": "2024-06-03", "date_to": "2024-06-06", "doi": "10.24132/CSRN.3401.7", "editor": "Skala, Vaclav", "event": "32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2024)", "lecturer": [ 1410 ], "location": "Plzen", "pages": "10", "pages_from": "57", "pages_to": "66", "research_areas": [], "keywords": [ "data visualization", "education for visualization" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/amabili-2024-smg/", "__class": "Publication" }, { "id": "muth-2024-edt", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/202187", "title": "Exploring Drusen Type and Appearance using Interpretable GANs", "date": "2024", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5391, 5392, 1410, 5393, 5394, 5395 ], "booktitle": "VCBM 2024: Eurographics Workshop on Visual Computing for Biology and Medicine", "date_from": "2024-09-19", "date_to": "2024-09-20", "doi": "10.2312/vcbm.20241187", "event": "Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM 2024)", "lecturer": [ 5396 ], "research_areas": [], "keywords": [ "Image Processing", "Machine Learning", "Ophthalmology" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/muth-2024-edt/", "__class": "Publication" }, { "id": "lucio-2024-kma", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/204917", "title": "Knowledge maps as a complementary tool to learn and teach surgical anatomy in virtual reality: A case study in dental implantology", "date": "2024", "abstract": "A thorough understanding of surgical anatomy is essential for preparing and training medical students to become competent and skilled surgeons. While Virtual Reality (VR) has shown to be a suitable interaction paradigm for surgical training, traditional anatomical VR models often rely on simple labels and arrows pointing to relevant landmarks. Yet, studies have indicated that such visual settings could benefit from knowledge maps as such representations explicitly illustrate the conceptual connections between anatomical landmarks. In this article, a VR educational tool is presented designed to explore the potential of knowledge maps as a complementary visual encoding for labeled 3D anatomy models. Focusing on surgical anatomy for implantology, it was investigated whether integrating knowledge maps within a VR environment could improve students' understanding and retention of complex anatomical relationships. The study involved 30 master's students in dentistry and 3 anatomy teachers, who used the tool and were subsequently assessed through surgical anatomy quizzes (measuring both completion times and scores) and subjective feedback (assessing user satisfaction, preferences, system usability, and task workload). The results showed that using knowledge maps in an immersive environment facilitates learning and teaching surgical anatomy applied to implantology, serving as a complementary tool to conventional VR educational methods.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5407, 5408, 1410, 5409, 5410, 5411, 5412, 5413 ], "booktitle": "Healthcare Technology Letters", "date_from": "2024-10-06", "date_to": "2024-10-06", "doi": "10.1049/htl2.12094", "event": "27th International Conference on Medical Image Computing and Computer Assisted Invertention (MICCAI 2024)", "lecturer": [ 5407 ], "pages": "12", "research_areas": [], "keywords": [ "biomedical education", "user interfaces", "virtual reality", "biomedical education", "user interfaces", "virtual reality" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/lucio-2024-kma/", "__class": "Publication" }, { "id": "kovacs-2024-gsg", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/205211", "title": "G-Style: Stylized Gaussian Splatting", "date": "2024", "abstract": "We introduce G-Style, a novel algorithm designed to transfer the style of an image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting is a powerful 3D representation for novel view synthesis, as—compared to other approaches based on Neural Radiance Fields—it provides fast scene renderings and user control over the scene. Recent pre-prints have demonstrated that the style of Gaussian Splatting scenes can be modified using an image exemplar. However, since the scene geometry remains fixed during the stylization process, current solutions fall short of producing satisfactory results. Our algorithm aims to address these limitations by following a three-step process: In a pre-processing step, we remove undesirable Gaussians with large projection areas or highly elongated shapes. Subsequently, we combine several losses carefully designed to preserve different scales of the style in the image, while maintaining as much as possible the integrity of the original scene content. During the stylization process and following the original design of Gaussian Splatting, we split Gaussians where additional detail is necessary within our scene by tracking the gradient of the stylized color. Our experiments demonstrate that G-Style generates high-quality stylizations within just a few minutes, outperforming existing methods both qualitatively and quantitatively.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1950, 5415, 1410 ], "articleno": "e15259", "doi": "10.1111/cgf.15259", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "7", "pages": "13", "publisher": "WILEY", "volume": "43", "research_areas": [], "keywords": [ "Artificial intelligence", "Computer graphics", "Neural networks" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/kovacs-2024-gsg/", "__class": "Publication" }, { "id": "tanaka-2024-vor", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/209946", "title": "Visualization of Relationships between Precipitation and River Water Levels", "date": "2024", "abstract": "Observation of precipitation changes is important for a variety of purposes such as predicting river levels. Previous studies for data visualization of precipitation and river water levels plotted graphs and color bars were many stations on a map. Instead of such visualizations on a map, we construct a graph to imitate a connected structure such as a tributary of a river in this study. Our method displays two pseudo-coloring sparklines at nodes of the graph as the stations. The method can visualize the time difference between the increase in precipitation upstream and the increase in river water level downstream. Users can observe precipitation and river water levels at different observation points. Our method uses a Delaunay diagram connecting gauging positions to interpolate and calculate precipitation at river level observation points. This avoids the discrepancy between observation points.In addition, we adjust the amount of visualized information by skipping the display of several observation points based on the similarity of the time-series data at each station, which is calculated by applying the dynamic time-stretching method. The visualization results show that downstream, once the water level rises, it tends to take longer for the water level to drop. In addition, the results show that a time lag occurs between the increase in precipitation and the rise in river levels in the mainstream, while tributaries have little time lag. In addition, data on rainfall and river levels at the same station over multiple periods and their relationship are plotted as scatter plots. The scatter plots make it easier to compare data from multiple periods at the same time than two-tone pseudo coloring sparklines.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5438, 1937, 1850, 1410, 166, 1754 ], "booktitle": "2024 28th International Conference Information Visualisation (IV)", "date_from": "2024-07-22", "date_to": "2024-07-26", "doi": "10.1109/IV64223.2024.00020", "event": "2024 28th International Conference Information Visualisation (IV)", "isbn": "979-8-3503-8016-3", "lecturer": [ 5438 ], "location": "Coimbra", "pages": "6", "pages_from": "58", "pages_to": "63", "publisher": "IEEE", "research_areas": [], "keywords": [ "Geographic Information", "Meteorological Information", "River Water level", "Interpolation", "Precipitation", "Rain", "Image color analysis", "Data visualization", "Rivers", "Space stations", "Bars" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/tanaka-2024-vor/", "__class": "Publication" }, { "id": "ehlers-2023-iro", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/192189", "title": "Improving readability of static, straight-line graph drawings: A first look at edge crossing resolution through iterative vertex splitting", "date": "2023-11", "abstract": "We present a novel vertex-splitting approach to iteratively resolve edge crossings in order to improve the readability of graph drawings. Dense graphs, even when small in size (10 to 15 nodes in size) quickly become difficult to read with increasing numbers of edges, and form so-called “hairballs”. The readability of a graph drawing is measured using many different quantitative aesthetic metrics. One such metric of particular importance is the number of edge crossings. Classical approaches to improving readability, such as the minimization of the number of edge crossings, focus on providing overviews of the input graph by aggregating or sampling vertices and/or edges. However, this simplification of the graph drawing does not allow for detailed views into the data, as not all vertices or edges are rendered, and also requires sophisticated interaction approaches to perform well. To avoid this, our locally optimal vertex splitting approach aims to minimize the number of remaining edge crossings while also minimizing the number of vertices that need to be split. In each iteration, we identify the vertex contributing the largest number of edge crossings, remove it, locate the embedding locations of said vertex's two split copies, and determine each copy's unique adjacency. We conduct a user study with 52 participants to evaluate whether vertex splitting affects users’ abilities to conduct a set of graph analytical tasks on graphs 12 nodes in size. Users were tasked with identifying a vertex's adjacency, determining the shared neighbors of two vertices, and checking the validity of a set of paths. We ultimately conclude that within the context of small, dense graphs, systematic vertex splitting is preferred by participants and even positively impacts user performance, though at the cost of the time taken per task.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1850, 5308, 1410, 1464 ], "doi": "10.1016/j.cag.2023.09.010", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "pages": "16", "pages_from": "448", "pages_to": "463", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "116", "research_areas": [ "NetVis" ], "keywords": [ "Edge crossings", "Graph aesthetics", "Network visualization", "Vertex splitting" ], "weblinks": [], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/ehlers-2023-iro/", "__class": "Publication" }, { "id": "el-sherbiny-2023-vai", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/193257", "title": "Visual Analytics for the Integrated Exploration and Sensemaking of Cancer Cohort Radiogenomics and Clinical Information", "date": "2023-09-19", "abstract": "We present a visual analytics (VA) framework for the comprehensive exploration and integrated analysis of radiogenomic and clinical data from a cancer cohort. Our framework aims to support the workflow of cancer experts and biomedical data scientists as they investigate cancer mechanisms. Challenges in the analysis of radiogenomic data, such as the heterogeneity and complexity of the data sets, hinder the exploration and sensemaking of the available patient information. These challenges can be answered through the field of VA, but approaches that bridge radiogenomic and clinical data in an interactive and flexible visual framework are still lacking. Our approach enables the integrated exploration and joint analysis of radiogenomic data and clinical information for knowledge discovery and hypothesis assessment through a flexible VA dashboard. We follow a user-centered design strategy, where we integrate domain knowledge into a semi-automated analytical workflow based on unsupervised machine learning to identify patterns in the patient data provided by our collaborating domain experts. An interactive visual interface further supports the exploratory and analytical process in a free and a hypothesis-driven manner. We evaluate the unsupervised machine learning models through similarity measures and assess the usability of the framework through use cases conducted with cancer experts. Expert feedback indicates that our framework provides suitable and flexible means for gaining insights into large and heterogeneous cancer cohort data, while also being easily extensible to other data sets.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5276, 5323, 5324, 5325, 1410 ], "booktitle": "VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine", "date_from": "2023-09-20", "date_to": "2023-09-22", "doi": "10.2312/vcbm.20231220", "event": "EG VCBM 2023", "isbn": "978-3-03868-177-9", "lecturer": [ 5276 ], "pages": "13", "pages_from": "121", "pages_to": "133", "publisher": "The Eurographics Association", "research_areas": [], "keywords": [ "Visual Analytics", "Human-centered computing", "Applied computing", "Life and medical sciences" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/el-sherbiny-2023-vai/", "__class": "Publication" }, { "id": "schindler-2023-sso", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/193253", "title": "Smoke Surfaces of 4D Biological Dynamical Systems", "date": "2023-09-19", "abstract": "To study biological phenomena, mathematical biologists often employ modeling with ordinary differential equations. A system of ordinary differential equations that describes the state of a phenomenon as a moving point in space across time is known as a dynamical system. This moving point emerges from the initial condition of the system and is referred to as a trajectory that “lives” in phase space, i.e., a space that defines all possible states of the system. In our previous work, we proposed ManyLands [AKS∗19]-an approach to explore and analyze typical trajectories of 4D dynamical systems, using smooth, animated transitions to navigate through phase space. However, in ManyLands the comparison of multiple trajectories emerging from different initial conditions does not scale well, due to overdrawing that clutters the view. We extend ManyLands to support the comparative visualization of multiple trajectories of a 4D dynamical system, making use of smoke surfaces. In this way, the sensitivity of the dynamical system to its initialization can be investigated. The 4D smoke surfaces can be further projected onto lower-dimensional subspaces (3D and 2D) with seamless animated transitions. We showcase the capabilities of our approach using two 4D dynamical systems from biology [Gol11, KJS06] and a 4D dynamical system exhibiting chaotic behavior [Bou15].", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1760, 1170, 1410 ], "booktitle": "VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine", "date_from": "2023-09-20", "date_to": "2023-09-22", "doi": "10.2312/vcbm.20231217", "editor": "Höllt, T. and Jönsson, D.", "event": "EG VCBM 2023", "isbn": "978-3-03868-010-9", "lecturer": [ 1760 ], "pages": "5", "pages_from": "93", "pages_to": "97", "publisher": "The Eurographics Association", "research_areas": [], "keywords": [ "rendering", "Human-centered computing", "scientific visualization", "Applied computing", "Life and medical sciences" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/schindler-2023-sso/", "__class": "Publication" }, { "id": "musleh-2023-ugi", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/158183", "title": "Uncertainty guidance in proton therapy planning visualization", "date": "2023-02-20", "abstract": "We investigate uncertainty guidance mechanisms to support proton therapy (PT) planning visualization. Uncertainties in the PT workflow pose significant challenges for navigating treatment plan data and selecting the most optimal plan among alternatives. Although guidance techniques have not yet been applied to PT planning scenarios, they have successfully supported sense- and decision-making processes in other contexts. We hypothesize that augmenting PT uncertainty visualization with guidance may influence the intended users' perceived confidence and provide new insights. To this end, we follow an iterative co-design process with domain experts to develop a visualization dashboard enhanced with distinct level-of-detail uncertainty guidance mechanisms. Our approach classifies uncertainty guidance into two dimensions: degree of intrusiveness and detail-orientation. Our dashboard supports the comparison of multiple treatment plans (i.e., nominal plans with their translational variations) while accounting for multiple uncertainty factors. We subsequently evaluate the designed and developed strategies by assessing perceived confidence and effectiveness during a sense- and decision-making process. Our findings indicate that uncertainty guidance in PT planning visualization does not necessarily impact the perceived confidence of the users in the process. Nonetheless, it provides new insights and raises uncertainty awareness during treatment plan selection. This observation was particularly evident for users with longer experience in PT planning.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Graphical Abstract", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1333, "image_height": 1039, "name": "musleh-2023-ugi-Graphical Abstract.jpg", "type": "image/jpeg", "size": 235392, "path": "Publication:musleh-2023-ugi", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/musleh-2023-ugi-Graphical Abstract.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/musleh-2023-ugi-Graphical Abstract:thumb{{size}}.png" }, "sync_repositum_override": "event", "repositum_presentation_id": null, "authors": [ 1867, 1445, 1863, 5236, 166, 1410 ], "doi": "10.1016/j.cag.2023.02.002", "event": "VCBM 2023", "issn": "1873-7684", "journal": "Computers and Graphics", "open_access": "yes", "pages_from": "166", "pages_to": "179", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "111", "research_areas": [ "MedVis" ], "keywords": [ "Visual analytics", "Applied computing", "Decision support systems" ], "weblinks": [], "files": [ { "description": null, "filetitle": "Graphical Abstract", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1333, "image_height": 1039, "name": "musleh-2023-ugi-Graphical Abstract.jpg", "type": "image/jpeg", "size": 235392, "path": "Publication:musleh-2023-ugi", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/musleh-2023-ugi-Graphical Abstract.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/musleh-2023-ugi-Graphical Abstract:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/", "__class": "Publication" }, { "id": "preim-2023-vva", "type_id": "book", "tu_id": null, "repositum_id": "20.500.12708/192952", "title": "Visualization, Visual Analytics and Virtual Reality in Medicine : State-Of-the-art Techniques and Applications", "date": "2023", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 563, 1410, 5320, 1544 ], "isbn": "9780128229620", "pages": "554", "publisher": "Elsevier", "research_areas": [], "keywords": [ "medical visualization", "virtual reality" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/preim-2023-vva/", "__class": "Publication" }, { "id": "stritzel2022", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Predicting, Analyzing and Communicating Outcomes of COVID-19 Hospitalizations with Medical Images and Clinical Data", "date": "2022-09-21", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 479, "image_height": 175, "name": "stritzel2022-.JPG", "type": "image/jpeg", "size": 21887, "path": "Publication:stritzel2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/stritzel2022-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/stritzel2022-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5188, 1410 ], "booktitle": "Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM2022).", "cfp": { "name": "Full, Short & Survey Papers.pdf", "type": "application/pdf", "error": "0", "size": "195197", "orig_name": "Full, Short & Survey Papers.pdf", "ext": "pdf" }, "event": "EG VCBM 2022", "lecturer": [ 1410 ], "pages_from": "129", "pages_to": "133", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [ { "href": "https://diglib.eg.org/handle/10.2312/vcbm20221196", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 479, "image_height": 175, "name": "stritzel2022-.JPG", "type": "image/jpeg", "size": 21887, "path": "Publication:stritzel2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/stritzel2022-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/stritzel2022-:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": true, "access": "public", "preview_image_width": 409, "preview_image_height": 458, "name": "stritzel2022-paper.pdf", "type": "application/pdf", "size": 1904424, "path": "Publication:stritzel2022", "preview_name": "stritzel2022-paper:preview.PNG", "preview_type": "image/png", "preview_size": 67566, "url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/stritzel2022-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/stritzel2022-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "supplementarty images ", "main_file": false, "use_in_gallery": false, "access": "public", "name": "stritzel2022-supplementarty images .pdf", "type": "application/pdf", "size": 364096, "path": "Publication:stritzel2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/stritzel2022-supplementarty images .pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/stritzel2022-supplementarty images :thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/stritzel2022/", "__class": "Publication" }, { "id": "magg2022", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/177098", "title": "Visual Analytics to Assess Deep Learning Models for Cross-Modal Brain Tumor Segmentation", "date": "2022-09-21", "abstract": "Accurate delineations of anatomically relevant structures are required for cancer treatment planning. Despite its accuracy, manual labeling is time-consuming and tedious-hence, the potential of automatic approaches, such as deep learning models, is being investigated. A promising trend in deep learning tumor segmentation is cross-modal domain adaptation, where knowledge learned on one source distribution (e.g., one modality) is transferred to another distribution. Yet, artificial intelligence (AI) engineers developing such models, need to thoroughly assess the robustness of their approaches, which demands a deep understanding of the model(s) behavior. In this paper, we propose a web-based visual analytics application that supports the visual assessment of the predictive performance of deep learning-based models built for cross-modal brain tumor segmentation. Our application supports the multi-level comparison of multiple models drilling from entire cohorts of patients down to individual slices, facilitates the analysis of the relationship between image-derived features and model performance, and enables the comparative exploration of the predictive outcomes of the models. All this is realized in an interactive interface with multiple linked views. We present three use cases, analyzing differences in deep learning segmentation approaches, the influence of the tumor size, and the relationship of other data set characteristics to the performance. From these scenarios, we discovered that the tumor size, i.e., both volumetric in 3D data and pixel count in 2D data, highly affects the model performance, as samples with small tumors often yield poorer results. Our approach is able to reveal the best algorithms and their optimal configurations to support AI engineers in obtaining more insights for the development of their segmentation models.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 628, "image_height": 188, "name": "magg2022-.JPG", "type": "image/jpeg", "size": 39077, "path": "Publication:magg2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1860, 1410 ], "booktitle": "Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM 2022)", "date_from": "2022-09-22", "date_to": "2022-09-23", "doi": "10.2312/vcbm.20221193", "event": "Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM2022)", "isbn": "978-3-03868-177-9", "lecturer": [ 1860 ], "location": "Wien", "open_access": "yes", "pages": "5", "pages_from": "111", "pages_to": "115", "publisher": "The Eurographics Association", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [ "Visual Analytics", "Life and medical sciences", "Applied computing" ], "weblinks": [ { "href": "https://diglib.eg.org/handle/10.2312/vcbm20221193", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 628, "image_height": 188, "name": "magg2022-.JPG", "type": "image/jpeg", "size": 39077, "path": "Publication:magg2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 190, "preview_image_height": 173, "name": "magg2022-paper.pdf", "type": "application/pdf", "size": 1438001, "path": "Publication:magg2022", "preview_name": "magg2022-paper:preview.PNG", "preview_type": "image/png", "preview_size": 41052, "url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "supplementarty video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "magg2022-supplementarty video.mp4", "type": "video/mp4", "size": 24107903, "path": "Publication:magg2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-supplementarty video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-supplementarty video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/magg2022-supplementarty video:video.mp4" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/magg2022/", "__class": "Publication" }, { "id": "borondy2022", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Understanding the impact of statistical and machine learning choices on predictive models for radiotherapy.", "date": "2022-09-21", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 384, "image_height": 494, "name": "borondy2022-.JPG", "type": "image/jpeg", "size": 34439, "path": "Publication:borondy2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/borondy2022/borondy2022-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/borondy2022/borondy2022-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5187, 1733, 1410 ], "booktitle": "Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM2022)", "cfp": { "name": "Full, Short & Survey Papers.pdf", "type": "application/pdf", "error": "0", "size": "195197", "orig_name": "Full, Short & Survey Papers.pdf", "ext": "pdf" }, "event": "EG VCBM 2022", "lecturer": [ 1733 ], "pages_from": "65", "pages_to": "69", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [ { "href": "https://diglib.eg.org/handle/10.2312/vcbm20221188", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 384, "image_height": 494, "name": "borondy2022-.JPG", "type": "image/jpeg", "size": 34439, "path": "Publication:borondy2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/borondy2022/borondy2022-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/borondy2022/borondy2022-:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": 409, "preview_image_height": 458, "name": "borondy2022-paper.pdf", "type": "application/pdf", "size": 5235751, "path": "Publication:borondy2022", "preview_name": "borondy2022-paper:preview.PNG", "preview_type": "image/png", "preview_size": 67566, "url": "https://www.cg.tuwien.ac.at/research/publications/2022/borondy2022/borondy2022-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/borondy2022/borondy2022-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/borondy2022/", "__class": "Publication" }, { "id": "martorell2022", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/150213", "title": "Breast cancer patient characterisation and visualisation using deep learning and fisher information networks", "date": "2022-08-17", "abstract": "Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification. However, the added complexity that makes DL models so successful reduces their ability to explain which features are relevant to the model, or whether the model is biased. The main aim of this study is to propose a novel visualisation to help characterise breast cancer patients using Fisher Information Networks on features extracted from mammograms using a DL model. In the proposed visualisation, patients are mapped out according to their similarities and can be used to study new patients as a 'patient-like-me' approach. When applied to the CBIS-DDSM dataset, it was shown that it is a competitive methodology that can (i) facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of the FIN visualisations and 'patient-like-me' analysis, and (ii) help improve diagnostic accuracy and reduce overdiagnosis by identifying the most likely diagnosis based on clinical similarities with neighbouring patients.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1283, "image_height": 829, "name": "martorell2022-.JPG", "type": "image/jpeg", "size": 126972, "path": "Publication:martorell2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/martorell2022/martorell2022-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/martorell2022/martorell2022-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5189, 5190, 5191, 1410, 5192, 5193, 5194, 5195, 5196 ], "doi": "10.1038/s41598-022-17894-6", "issn": "2045-2322", "journal": "Scientific Reports", "open_access": "yes", "pages": "14", "publisher": "Nature Publishing", "volume": "12", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [ "Breast", "Female", "Humans", "Information Services", "Mammography", "Breast Neoplasms", "Deep Learning" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1283, "image_height": 829, "name": "martorell2022-.JPG", "type": "image/jpeg", "size": 126972, "path": "Publication:martorell2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/martorell2022/martorell2022-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/martorell2022/martorell2022-:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/martorell2022/", "__class": "Publication" }, { "id": "Schindler-2022-eurovis", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/175711", "title": "Nested Papercrafts for Anatomical and Biological Edutainment", "date": "2022-06", "abstract": "In this paper, we present a new workflow for the computer-aided generation of physicalizations, addressing Nested configurations in anatomical and biological structures. Physicalizations are an important component of anatomical and biological education and edutainment. However, existing approaches have mainly revolved around creating data sculptures through digital fabrication. Only a few recent works proposed computer-aided pipelines for generating sculptures, such as papercrafts, with affordable and readily available materials. Papercraft generation remains a Challenging topic by itself. Yet, anatomical and biological applications pose additional Challenges, such as reconstruction complexity and insufficiency to account for multiple, Nested structures—often present in anatomical and biological structures. Our workflow comprises the following steps: (i) define the Nested configuration of the model and detect its levels, (ii) calculate the viewpoint that provides optimal, unobstructed views on inner levels, (iii) perform cuts on the outer levels to reveal the inner ones based on the viewpoint selection, (iv) estimate the stability of the cut papercraft to ensure a reliable outcome, (v) generate textures at each level, as a smart visibility mechanism that provides additional information on the inner structures, and (vi) unfold each textured mesh guaranteeing reconstruction. Our novel approach exploits the interactivity of Nested papercraft models for edutainment purposes.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 256, "image_height": 192, "name": "Schindler-2022-eurovis-image.png", "type": "image/png", "size": 66402, "path": "Publication:Schindler-2022-eurovis", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-image:thumb{{size}}.png" }, "sync_repositum_override": "lecturer,journal", "repositum_presentation_id": null, "authors": [ 1760, 1697, 1410, 1464 ], "booktitle": "Eurographics Conference on Visualization (EuroVis) 2022", "cfp": { "name": "ev2022-cpf.pdf", "type": "application/pdf", "error": "0", "size": "241772", "orig_name": "ev2022-cpf.pdf", "ext": "pdf" }, "date_from": "2022-06-13", "date_to": "2022-06-17", "doi": "10.1111/cgf.14561", "event": "EuroVis 2022 - 24th Eurographics Conference on Visualization", "journal": "Computer Graphics Forum", "lecturer": [ 1464 ], "location": "Rom", "open_access": "yes", "pages": "13", "pages_from": "541", "pages_to": "553", "publisher": "Wiley & Sons Ltd", "volume": "41,3", "research_areas": [ "MedVis" ], "keywords": [ "Applied computing", "Life and medical sciences", "Human-centered computing", "Visualization techniques", "Scientific visualization", "Interaction paradigms" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 256, "image_height": 192, "name": "Schindler-2022-eurovis-image.png", "type": "image/png", "size": 66402, "path": "Publication:Schindler-2022-eurovis", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "Schindler-2022-eurovis-paper.pdf", "type": "application/pdf", "size": 46112659, "path": "Publication:Schindler-2022-eurovis", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "Schindler-2022-eurovis-video.mp4", "type": "video/mp4", "size": 76649209, "path": "Publication:Schindler-2022-eurovis", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/Schindler-2022-eurovis-video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Schindler-2022-eurovis/", "__class": "Publication" }, { "id": "boeroendy-2022-uio", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/175674", "title": "Understanding the impact of statistical and machine learning choices on predictive models for radiotherapy", "date": "2022", "abstract": "During radiotherapy (RT) planning, an accurate description of the location and shape of the pelvic organs is a critical factor for the successful treatment of the patient. Yet, during treatment, the pelvis anatomy may differ significantly from the planning phase. A series of recent publications, such as PREVIS [FMCM∗21], have examined alternative approaches to analyzing and predicting pelvic organ variability of individual patients. These approaches are based on a combination of several statistical and machine learning methods, which have not been thoroughly and quantitatively evaluated within the scope of pelvic anatomical variability. Several of their design decisions could have an impact on the outcome of the predictive model. The goal of this work is to assess the impact of alternative choices, focusing mainly on the two key-aspects of shape description and clustering, to generate better predictions for new patients. The results of our assessment indicate that resolution-based descriptors provide more accurate and reliable organ representations than state-of-the-art approaches, while different clustering settings (distance metric and linkage) yield only slightly different clusters. Different clustering methods are able to provide comparable results, although when more shape variability is considered their results start to deviate. These results are valuable for understanding the impact of statistical and machine learning choices on the outcomes of predictive models for anatomical variability.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "keywords,projects", "repositum_presentation_id": null, "authors": [ 5187, 1733, 1410 ], "booktitle": "VCBM 2022: Eurographics Workshop on Visual Computing for Biology and Medicine", "date_from": "2022-09-22", "date_to": "2022-09-23", "doi": "10.2312/vcbm.20221188", "event": "Eurographics Workshop on Visual Computing for Biology and Medicine (2022)", "isbn": "978-3-03868-177-9", "lecturer": [ 5187 ], "location": "Wien", "open_access": "yes", "pages": "5", "pages_from": "65", "pages_to": "69", "publisher": "The Eurographics Association", "volume": "2022", "research_areas": [], "keywords": [ "Human-centered computing", "Visual Analytics", "Life and medical sciences", "Applied computing" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "boeroendy-2022-uio-paper.pdf", "type": "application/pdf", "size": 5235751, "path": "Publication:boeroendy-2022-uio", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/boeroendy-2022-uio/boeroendy-2022-uio-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/boeroendy-2022-uio/boeroendy-2022-uio-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/boeroendy-2022-uio/", "__class": "Publication" }, { "id": "amabili2021", "type_id": "journalpaper_notalk", "tu_id": 300122, "repositum_id": "20.500.12708/138525", "title": "A Taxonomy-Driven Model for Designing Educational Games in Visualization", "date": "2021-10-01", "abstract": "Visualization literacy of the broader audiences is becoming an important topic, as we are increasingly surrounded by misleading, erroneous, or confusing visualizations. How can we educate the general masses about data visualization? We propose a twofold model for designing educational games in visualization based on the concept of constructivism and learning-by-playing. We base our approach on the idea of deconstruction and construction, borrowed from the domain of mathematics. We describe the conceptual development and design of our model through two games. First, we present a deconstruction-based game that requires the inspection, identification, and categorization of visualization characteristics (data, users, tasks, visual variables, visualization vocabulary), starting from a finalized visualization. Second, we propose a construction-based game where it is\npossible to compose visualizations bottom-up from individual visual characteristics. The two games use the same deck of cards with a simple design\nbased on visualization taxonomies, popular in visualization teaching.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": 293, "preview_image_height": 275, "name": "amabili2021-.pdf", "type": "application/pdf", "size": 10705214, "path": "Publication:amabili2021", "preview_name": "amabili2021-:preview.PNG", "preview_type": "image/png", "preview_size": 62760, "url": "https://www.cg.tuwien.ac.at/research/publications/2021/amabili2021/amabili2021-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/amabili2021/amabili2021-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1861, 1862, 1410 ], "doi": "https://doi.org/10.1109/mcg.2021.3115446", "first_published": "2021-10-01", "journal": "IEEE Computer Graphics and Applications", "number": "6", "open_access": "yes", "pages_from": "71", "pages_to": "79", "volume": "41", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": 293, "preview_image_height": 275, "name": "amabili2021-.pdf", "type": "application/pdf", "size": 10705214, "path": "Publication:amabili2021", "preview_name": "amabili2021-:preview.PNG", "preview_type": "image/png", "preview_size": 62760, "url": "https://www.cg.tuwien.ac.at/research/publications/2021/amabili2021/amabili2021-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/amabili2021/amabili2021-:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/amabili2021/", "__class": "Publication" }, { "id": "pahr-2021-vologram", "type_id": "inproceedings", "tu_id": 300436, "repositum_id": "20.500.12708/58633", "title": "Vologram: An Educational Holographic Sculpture for Volumetric Medical Data Physicalization", "date": "2021-09", "abstract": "Real-world sculptures that display patient imaging data for anatomical education purposes have seen a recent resurgence through the field of data physicalization. In this paper, we describe an automated process for the computer-assisted generation of sculptures that can be employed for anatomical education among the general population. We propose a workflow that supports non-expert users to generate and physically display volumetric medical data in a visually appealing and engaging way. Our approach generates slide-based, interactive sculptures-called volograms-that resemble holograms of underlying medical data. The volograms are made out of affordable and readily available materials (e.g., transparent foils and cardboard) and can be produced through commonly available means. To evaluate the educational value of the proposed approach with our target audience, we assess the volograms, as opposed to classical, on-screen medical visualizations in a user study. The results of our study, while highlighting current weaknesses of our physicalization, also point to interesting future directions.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 4032, "image_height": 3024, "name": "pahr-2021-vologram-Image.JPG", "type": "image/jpeg", "size": 954594, "path": "Publication:pahr-2021-vologram", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/pahr-2021-vologram/pahr-2021-vologram-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/pahr-2021-vologram/pahr-2021-vologram-Image:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1813, 1464, 1410 ], "booktitle": "Eurographics Workshop on Visual Computing for Biology and Medicine", "cfp": { "name": "Full, Short & Survey Papers – VCBM 2021.pdf", "type": "application/pdf", "error": "0", "size": "446938", "orig_name": "Full, Short & Survey Papers – VCBM 2021.pdf", "ext": "pdf" }, "doi": "10.2312/vcbm.20211341", "event": "Eurographics Workshop on Visual Computing for Biology and Medicine", "issn": "2070-5786", "journal": "Eurographics Workshop on Visual Computing for Biology and Medicine", "lecturer": [ 1813 ], "pages": "5", "pages_from": "19", "pages_to": "23", "publisher": "Eurographics Association", "volume": "VCBM 2021", "research_areas": [ "Fabrication", "IllVis", "MedVis" ], "keywords": [ "Physicalization", "Anatomy Education" ], "weblinks": [ { "href": "https://doi.org/10.2312/vcbm.20211341", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "Image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 4032, "image_height": 3024, "name": "pahr-2021-vologram-Image.JPG", "type": "image/jpeg", "size": 954594, "path": "Publication:pahr-2021-vologram", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/pahr-2021-vologram/pahr-2021-vologram-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/pahr-2021-vologram/pahr-2021-vologram-Image:thumb{{size}}.png" }, { "description": null, "filetitle": "Video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "pahr-2021-vologram-Video.mp4", "type": "video/mp4", "size": 138438430, "path": "Publication:pahr-2021-vologram", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/pahr-2021-vologram/pahr-2021-vologram-Video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/pahr-2021-vologram/pahr-2021-vologram-Video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2021/pahr-2021-vologram/pahr-2021-vologram-Video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/pahr-2021-vologram/", "__class": "Publication" }, { "id": "magg2021", "type_id": "inproceedings", "tu_id": 300509, "repositum_id": "20.500.12708/58638", "title": "Visual Assessment of Growth Prediction in Brain Structures after Pediatric Radiotherapy", "date": "2021-09", "abstract": "Pediatric brain tumor radiotherapy research is investigating how radiation influences the development and function of a pa-\ntient’s brain. To better understand how brain growth is affected by the treatment, the brain structures of the patient need to be\nexplored and analyzed pre- and post-treatment. In this way, anatomical changes are observed over a long period, and are as-\nsessed as potential early markers of cognitive or functional damage. In this early work, we propose an automated approach for\nthe visual assessment of the growth prediction of brain structures in pediatric brain tumor radiotherapy patients. Our approach\nreduces the need for re-segmentation, and the time required for it. We employ as a basis pre-treatment Computed Tomography\n(CT) scans with manual delineations (i.e., segmentation masks) of specific brain structures of interest. These pre-treatment\nmasks are used as initialization, to predict the corresponding masks on multiple post-treatment follow-up Magnetic Resonance\n(MR) images, using an active contour model approach. For the accuracy quantification of the automatically predicted post-\ntreatment masks, a support vector regressor (SVR) with features related to geometry, intensity, and gradients is trained on the\npre-treatment data. Finally, a distance transform is employed to calculate the distances between pre- and post-treatment data\nand to visualize the predicted growth of a brain structure, along with its respective accuracy. Although segmentations of larger\nstructures are more accurately predicted, the growth behavior of all structures is learned correctly, as indicated by the SVR\nresults. This suggests that our pipeline is a positive initial step for the visual assessment of brain structure growth predictio", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 609, "preview_image_height": 541, "name": "magg2021-.pdf", "type": "application/pdf", "size": 1423124, "path": "Publication:magg2021", "preview_name": "magg2021-:preview.PNG", "preview_type": "image/png", "preview_size": 209086, "url": "https://www.cg.tuwien.ac.at/research/publications/2021/magg2021/magg2021-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/magg2021/magg2021-:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1860, 1863, 1445, 1864, 1410 ], "booktitle": "Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM2021).", "cfp": { "name": "Full, Short & Survey Papers – VCBM 2021.pdf", "type": "application/pdf", "error": "0", "size": "229013", "orig_name": "Full, Short & Survey Papers – VCBM 2021.pdf", "ext": "pdf" }, "doi": "10.2312/vcbm.20211343", "event": "EG VCBM 2021", "lecturer": [ 1860 ], "pages": "5", "pages_from": "31", "pages_to": "35", "publisher": "Eurographics Association", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 609, "preview_image_height": 541, "name": "magg2021-.pdf", "type": "application/pdf", "size": 1423124, "path": "Publication:magg2021", "preview_name": "magg2021-:preview.PNG", "preview_type": "image/png", "preview_size": 209086, "url": "https://www.cg.tuwien.ac.at/research/publications/2021/magg2021/magg2021-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/magg2021/magg2021-:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/magg2021/", "__class": "Publication" }, { "id": "raidou_previs2021", "type_id": "journalpaper_notalk", "tu_id": 301682, "repositum_id": "20.500.12708/138838", "title": "PREVIS: Predictive visual analytics of anatomical variability for radiotherapy decision support", "date": "2021-04", "abstract": "adiotherapy (RT) requires meticulous planning prior to treatment, where the RT plan is optimized with organ delineations on a pre-treatment Computed Tomography (CT) scan of the patient. The conventionally fractionated treatment usually lasts several weeks. Random changes (e.g., rectal and bladder filling in prostate cancer patients) and systematic changes (e.g., weight loss) occur while the patient is being treated. Therefore, the delivered dose distribution may deviate from the planned. Modern technology, in particular image guidance, allows to minimize these deviations, but risks for the patient remain.\n\nWe present PREVIS, a visual analytics tool for:\n\n(i) the exploration and prediction of changes in patient anatomy during the upcoming treatment, and\n\n(ii) the assessment of treatment strategies, with respect to the anticipated changes.\n\nRecords of during-treatment changes from a retrospective imaging cohort with complete data are employed in PREVIS, to infer expected anatomical changes of new incoming patients with incomplete data, using a generative model. Abstracted representations of the retrospective cohort partitioning provide insight into an underlying automated clustering, showing main modes of variation for past patients. Interactive similarity representations support an informed selection of matching between new incoming patients and past patients. A Principal Component Analysis (PCA)-based generative model describes the predicted spatial probability distributions of the incoming patient’s organs in the upcoming weeks of treatment, based on observations of past patients. The generative model is interactively linked to treatment plan evaluation, supporting the selection of the optimal treatment strategy.\n\nWe present a usage scenario, demonstrating the applicability of PREVIS in a clinical research setting, and we evaluate our visual analytics tool with eight clinical researchers.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1733, 1445, 1444, 1568, 1569, 1844, 1410 ], "doi": "https://doi.org/10.1016/j.cag.2021.04.010", "journal": "Computers and Graphics", "open_access": "yes", "pages_from": "126", "pages_to": "138", "volume": "97", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [ { "href": "https://renataraidou.com/previs-predictive-visual-analytics-of-anatomical-variability-for-radiotherapy-decision-support/", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": 768, "preview_image_height": 590, "name": "raidou_previs2021-paper.pdf", "type": "application/pdf", "size": null, "path": "Publication:raidou_previs2021", "preview_name": "raidou_previs2021-paper:preview.png", "preview_type": "image/png", "preview_size": 184775, "url": "https://www.cg.tuwien.ac.at/research/publications/2021/raidou_previs2021/raidou_previs2021-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/raidou_previs2021/raidou_previs2021-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/raidou_previs2021/", "__class": "Publication" }, { "id": "wu-2021-vi", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/138953", "title": "Visualization working group at TU Wien: Visibile Facimus Quod Ceteri Non Possunt", "date": "2021-03", "abstract": "Building-up and running a university-based research group is a multi-faceted undertaking. The visualization working group at TU Wien (vis-group) has been internationally active over more than 25 years. The group has been acting in a competitive scientific setting where sometimes contradicting multiple objectives require trade-offs and optimizations. Research-wise the group has been performing basic and applied research in visualization and visual computing. Teaching-wise the group has been involved in undergraduate and graduate lecturing in (medical) visualization and computer graphics. To be scientifically competitive requires to constantly expose the group and its members to a strong international competition at the highest level. This necessitates to shield the members against the ensuing pressures and demands and provide (emotional) support and encouragement. Internally, the vis-group has developed a unique professional and social interaction culture: work and celebrate, hard and together. This has crystallized into a nested, recursive, and triangular organization model, which concretizes what it takes to make a research group successful. The key elements are the creative and competent vis-group members who collaboratively strive for (scientific) excellence in a socially enjoyable environment.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 256, "image_height": 192, "name": "wu-2021-vi-image.png", "type": "image/png", "size": 37909, "path": "Publication:wu-2021-vi", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/wu-2021-vi-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/wu-2021-vi-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1464, 1170, 1366, 1285, 1383, 1263, 1577, 935, 1410, 171, 1110, 166 ], "doi": "https://doi.org/10.1016/j.visinf.2021.02.003", "journal": "Visual Informatics", "open_access": "yes", "pages_from": "76", "pages_to": "84", "volume": "5", "research_areas": [], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 256, "image_height": 192, "name": "wu-2021-vi-image.png", "type": "image/png", "size": 37909, "path": "Publication:wu-2021-vi", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/wu-2021-vi-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/wu-2021-vi-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "wu-2021-vi-paper.pdf", "type": "application/pdf", "size": 4057176, "path": "Publication:wu-2021-vi", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/wu-2021-vi-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/wu-2021-vi-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/", "__class": "Publication" }, { "id": "schindler_2020vis", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/58250", "title": "The Anatomical Edutainer", "date": "2020-10", "abstract": "Physical visualizations (i.e., data representations by means of physical objects) have been used for many centuries in medical and anatomical education. Recently, 3D printing techniques started also to emerge. Still, other medical physicalizations that rely on affordable and easy-to-find materials are limited, while smart strategies that take advantage of the optical properties of our physical world have not been thoroughly investigated. We propose the Anatomical Edutainer, a workflow to guide the easy, accessible, and affordable generation of physicalizations for tangible, interactive anatomical edutainment. The Anatomical Edutainer supports 2D printable and 3D foldable physicalizations that change their visual properties (i.e., hues of the visible spectrum) under colored lenses or colored lights, to reveal distinct anatomical structures through user interaction.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 256, "image_height": 192, "name": "schindler_2020vis-image.png", "type": "image/png", "size": 75476, "path": "Publication:schindler_2020vis", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/schindler_2020vis/schindler_2020vis-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/schindler_2020vis/schindler_2020vis-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1760, 1464, 1410 ], "booktitle": "IEEE Vis Short Papers 2020", "cfp": { "name": "Short Paper Call for Participation.pdf", "type": "application/pdf", "error": "0", "size": "132515", "orig_name": "Short Paper Call for Participation.pdf", "ext": "pdf" }, "event": "IEEE Vis 2020", "lecturer": [ 1410 ], "pages_from": "1", "pages_to": "5", "research_areas": [ "Fabrication", "IllVis", "MedVis", "Perception" ], "keywords": [ "Data Physicalization", "Medical Visualization", "Anatomical Education" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 256, "image_height": 192, "name": "schindler_2020vis-image.png", "type": "image/png", "size": 75476, "path": "Publication:schindler_2020vis", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/schindler_2020vis/schindler_2020vis-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/schindler_2020vis/schindler_2020vis-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper preprint", "main_file": true, "use_in_gallery": false, "access": "public", "preview_image_width": 416, "preview_image_height": 421, "name": "schindler_2020vis-paper preprint.pdf", "type": "application/pdf", "size": 26317989, "path": "Publication:schindler_2020vis", "preview_name": "schindler_2020vis-paper preprint:preview.JPG", "preview_type": "image/jpeg", "preview_size": 43449, "url": "https://www.cg.tuwien.ac.at/research/publications/2020/schindler_2020vis/schindler_2020vis-paper preprint.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/schindler_2020vis/schindler_2020vis-paper preprint:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/schindler_2020vis/", "__class": "Publication" }, { "id": "raidou_slicedice", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/140980", "title": "Slice and Dice: A PhysicalizationWorkflow for Anatomical Edutainment", "date": "2020-10", "abstract": "During the last decades, anatomy has become an interesting topic in education—even for laymen or schoolchildren. As medical imaging techniques become increasingly sophisticated, virtual anatomical education applications have emerged. Still, anatomical models are often preferred, as they facilitate 3D localization of anatomical structures. Recently, data physicalizations (i.e., physical visualizations) have proven to be effective and engaging—sometimes, even more than their virtual counterparts. So far, medical data physicalizations involve mainly 3D printing, which is still expensive and cumbersome. We investigate alternative forms of physicalizations, which use readily available technologies (home printers) and inexpensive materials (paper or semi-transparent films) to generate crafts for anatomical edutainment. To the best of our knowledge, this is the first computer-generated crafting approach within an anatomical edutainment context. Our approach follows a cost-effective, simple, and easy-to-employ workflow, resulting in assemblable data sculptures (i.e., semi-transparent sliceforms). It primarily supports volumetric data (such as CT or MRI), but mesh data can also be imported. An octree slices the imported volume and an optimization step simplifies the slice configuration, proposing the optimal order for easy assembly. A packing algorithm places the resulting slices with their labels, annotations, and assembly instructions on a paper or transparent film of user-selected size, to be printed, assembled into a sliceform, and explored. We conducted two user studies to assess our approach, demonstrating that it is an initial positive step towards the successful creation of interactive and engaging anatomical physicalizations.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 256, "image_height": 195, "name": "raidou_slicedice-image.png", "type": "image/png", "size": 51885, "path": "Publication:raidou_slicedice", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 166, 1464 ], "cfp": { "name": "For Authors - PG2020.pdf", "type": "application/pdf", "error": "0", "size": "248544", "orig_name": "For Authors - PG2020.pdf", "ext": "pdf" }, "date_from": "2020", "date_to": "2020", "event": "PG2020", "journal": "Computer Graphics Forum (CGF)", "lecturer": [ 1410 ], "pages_from": "1", "pages_to": "12", "volume": "x", "research_areas": [ "MedVis" ], "keywords": [ "Data Physicalization", "Life and Medical Sciences", "Anatomical Education" ], "weblinks": [], "files": [ { "description": null, "filetitle": "example", "main_file": true, "use_in_gallery": false, "access": "public", "name": "raidou_slicedice-example.pdf", "type": "application/pdf", "size": 1447424, "path": "Publication:raidou_slicedice", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-example.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-example:thumb{{size}}.png" }, { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 256, "image_height": 195, "name": "raidou_slicedice-image.png", "type": "image/png", "size": 51885, "path": "Publication:raidou_slicedice", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "preview_image_width": 3045, "preview_image_height": 3045, "name": "raidou_slicedice-paper.pdf", "type": "application/pdf", "size": 20867356, "path": "Publication:raidou_slicedice", "preview_name": "raidou_slicedice-paper:preview.jpg", "preview_type": "image/jpeg", "preview_size": 1021219, "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_slicedice-video.mp4", "type": "video/mp4", "size": 166880657, "path": "Publication:raidou_slicedice", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/raidou_slicedice-video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_slicedice/", "__class": "Publication" }, { "id": "furmanova_2020", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/140976", "title": "VAPOR: Visual Analytics for the Exploration of Pelvic Organ Variability in Radiotherapy", "date": "2020-10", "abstract": "In radiation therapy (RT) for prostate cancer, changes in patient anatomy during treatment might lead to inadequate tumor coverage and higher irradiation of healthy tissues in the nearby pelvic organs. Exploring and analyzing anatomical variability throughout the course of RT can support the design of more robust treatment strategies, while identifying patients that are prone to radiation-induced toxicity. We present VAPOR, a novel application for the exploration of pelvic organ variability in a cohort of patients, across the entire treatment process. Our application addresses: (i) the global exploration and analysis of anatomical variability in an abstracted tabular view, (ii) the local exploration and analysis thereof in anatomical 2D/3D views, where comparative and ensemble visualizations are integrated, and (iii) the correlation of anatomical variability with radiation doses and potential toxicity. The workflow is based on available retrospective cohort data, which include segmentations of the bladder, the prostate, and the rectum through the entire treatment period. VAPOR is applied to four usage scenarios, which were conducted with two medical physicists. Our application provides clinical researchers with promising support in demonstrating the significance of treatment adaptation to anatomical changes.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1733, 1366, 1445, 1444, 1568, 1569, 166, 1410 ], "doi": "https://doi.org/10.1016/j.cag.2020.07.001", "journal": "Computer & Graphics", "note": "Special Section on VCBM 2019", "pages_from": "25", "pages_to": "38", "volume": "91", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 1186, "preview_image_height": 641, "name": "furmanova_2020-paper.pdf", "type": "application/pdf", "size": 3355311, "path": "Publication:furmanova_2020", "preview_name": "furmanova_2020-paper:preview.PNG", "preview_type": "image/png", "preview_size": 382697, "url": "https://www.cg.tuwien.ac.at/research/publications/2020/furmanova_2020/furmanova_2020-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/furmanova_2020/furmanova_2020-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/furmanova_2020/", "__class": "Publication" }, { "id": "raidou_pingu2020", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/58256", "title": "PINGU Principles of Interactive Navigation for Geospatial Understanding", "date": "2020-06", "abstract": "Monitoring conditions in the periglacial areas of Antarctica helps geographers and geologists to understand physical processes associated with mesoscale land systems. Analyzing these unique temporal datasets poses a significant challenge for domain experts, due to the complex and often incomplete data, for which corresponding exploratory tools are not available. In this paper, we present a novel visual analysis tool for extraction and interactive exploration of temporal measurements captured at the polar station at the James Ross Island in Antarctica. The tool allows domain experts to quickly extract information about the snow level, originating from a series of photos acquired by trail cameras. Using linked views, the domain experts can interactively explore and combine this information with other spatial and non-spatial measures, such as temperature or wind speed, to reveal the interplay of periglacial and aeolian processes. An abstracted interactive map of the area indicates the position of measurement spots to facilitate navigation. The design of the tool was made in tight collaboration with geographers, which resulted in an early prototype, tested in the pilot study. The following version of the tool and its usability has been evaluated in the user study with five domain experts and their feedback was incorporated into the final version, presented in this paper. This version was again discussed with two experts in an informal interview. Within these evaluations, they confirmed the significant benefit of the tool for their research tasks.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1766, 1767, 1768, 1769, 1254, 1410, 1248 ], "booktitle": "2020 IEEE Pacific Visualization Symposium (PacificVis)", "cfp": { "name": "PacificVis 2020 call.pdf", "type": "application/pdf", "error": "0", "size": "168569", "orig_name": "PacificVis 2020 call.pdf", "ext": "pdf" }, "doi": "10.1109/PacificVis48177.2020.7567", "event": "IEEE Pacific Vis 2020", "lecturer": [ 1410 ], "pages_from": "216", "pages_to": "225", "research_areas": [ "InfoVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1899, "image_height": 943, "name": "raidou_pingu2020-image.bmp", "type": "image/bmp", "size": 5375154, "path": "Publication:raidou_pingu2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_pingu2020/raidou_pingu2020-image.bmp", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_pingu2020/raidou_pingu2020-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_pingu2020-paper.pdf", "type": "application/pdf", "size": 2925903, "path": "Publication:raidou_pingu2020", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_pingu2020/raidou_pingu2020-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_pingu2020/raidou_pingu2020-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_pingu2020/", "__class": "Publication" }, { "id": "raidou_visgap2020", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/58269", "title": "Lessons Learnt from Developing Visual Analytics Applications for Adaptive Prostate Cancer Radiotherapy", "date": "2020-05", "abstract": "In radiotherapy (RT), changes in patient anatomy throughout the treatment period might lead to deviations between planned\nand delivered dose, resulting in inadequate tumor coverage and/or overradiation of healthy tissues. Adapting the treatment to\naccount for anatomical changes is anticipated to enable higher precision and less toxicity to healthy tissues. Corresponding\ntools for the in-depth exploration and analysis of available clinical cohort data were not available before our work. In this\npaper, we discuss our on-going process of introducing visual analytics to the domain of adaptive RT for prostate cancer. This\nhas been done through the design of three visual analytics applications, built for clinical researchers working on the deployment\nof robust RT treatment strategies. We focus on describing our iterative design process, and we discuss the lessons learnt from\nour fruitful collaboration with clinical domain experts and industry, interested in integrating our prototypes into their workflow.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1733, 1366, 1444, 1568, 1569, 166, 1445 ], "booktitle": "The Gap between Visualization Research and Visualization Software (VisGap) (2020)", "cfp": { "name": "VisGap Workshop 2020.pdf", "type": "application/pdf", "error": "0", "size": "183556", "orig_name": "VisGap Workshop 2020.pdf", "ext": "pdf" }, "event": "EGEV2020 - VisGap Workshop", "lecturer": [ 1410 ], "open_access": "yes", "pages_from": "1", "pages_to": "8", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [ "Visual Analytics", "Life and Medical Sciences" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": true, "access": "public", "preview_image_width": 1837, "preview_image_height": 998, "name": "raidou_visgap2020-paper.pdf", "type": "application/pdf", "size": 2068564, "path": "Publication:raidou_visgap2020", "preview_name": "raidou_visgap2020-paper:preview.PNG", "preview_type": "image/png", "preview_size": 87558, "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_visgap2020/raidou_visgap2020-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_visgap2020/raidou_visgap2020-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_visgap2020/", "__class": "Publication" }, { "id": "raidou_shonan167", "type_id": "misc", "tu_id": null, "repositum_id": null, "title": "NII Shonan Meeting Report No. 167: Formalizing Biological and Medical Visualization", "date": "2020-02", "abstract": "Medicine and biology are among the most important research fields, having a significant impact on humans and their health. For decades, these fields have been highly dependent on visualization—establishing a tight coupling which is crucial for the development of visualization techniques, designed exclusively for the disciplines of medicine and biology. These visualization techniques can be generalized by the term Biological and Medical Visualization—for short,BioMedical Visualization. BioMedical Visualization is not only an enabler for medical diagnosis and treatment, but also an influential component of today’s life science research. Many BioMedical domains can now be studied at various scales and dimensions, with different imaging modalities and simulations, and for a variety of purposes. Accordingly, BioMedical Visualization has also innumerable contributions in industrial applications. However, despite its proven scientific maturity and societal value, BioMedical Visualization is often treated within Computer Science as a mere application subdomain of the broader field of Visualization.To enable BioMedical Visualization to further thrive, it is important to formalize its characteristics independently from the general field of Visualization.Also, several lessons learnt within the context of BioMedical Visualization may be applicable and extensible to other application domains or to the parent field of Visualization. Formalization has become particularly urgent, with the latest advances of BioMedical Visualization—in particular, with respect to dealing with Big Data Visualization, e.g., for the visualization of multi-scale, multi-modal,cohort, or computational biology data. Rapid changes and new opportunities in the field, also regarding the incorporation of Artificial Intelligence with“human-in-the-loop” concepts within the field of Visual Analytics, compel further this formalization. By enabling the BioMedical Visualization community to have intensive discussions on the systematization of current knowledge, we can adequately prepare ourselves for future prospects and challenges, while also contributing to the broader Visualization community.\nDuring this 4-day seminar, which was the 150th NII Shonan meeting to be organized, we brought together 25 visualization experts from diverse institutions,backgrounds and expertise to discuss, identify, formalize, and document the specifics of our field. This has been a great opportunity to cover a range of relevant and contemporary topics, and as a systematic effort towards establishing better fundaments for the field and towards determining novel future challenges.In the upcoming sections of this report, we summarize the content of invited talks and of the eight main topics that were discussed within the working groups during the seminar.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1248, 818, 951, 590 ], "note": "ISSN 2186-7437", "number": "TR-193-02-2020-1", "open_access": "yes", "research_areas": [ "BioVis", "MedVis" ], "keywords": [], "weblinks": [ { "href": "https://shonan.nii.ac.jp/seminars/167/", "caption": "workshop page", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "report", "main_file": false, "use_in_gallery": true, "access": "public", "preview_image_width": 2497, "preview_image_height": 1361, "name": "raidou_shonan167-report.pdf", "type": "application/pdf", "size": 2764706, "path": "Publication:raidou_shonan167", "preview_name": "raidou_shonan167-report:preview.PNG", "preview_type": "image/png", "preview_size": 332855, "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_shonan167/raidou_shonan167-report.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_shonan167/raidou_shonan167-report:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_shonan167/", "__class": "Publication" }, { "id": "raidou_2020Onc", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/141410", "title": "Principles of Visualization in Radiation Oncology", "date": "2020-01-15", "abstract": "Background: Medical visualization employs elements from computer graphics to create meaningful, interactive visual representations of medical data, and it has become an influential field of research for many advanced applications like radiation oncology, among others. Visual representations employ the user’s cognitive capabilities to support and accelerate diagnostic, planning, and quality assurance workflows based on involved patient data. Summary: This article discusses the basic underlying principles of visualization in the application domain of radiation oncology. The main visualization strategies, such as slice-based representations and surface and volume rendering are presented. Interaction topics, i.e., the combination of visualization and automated analysis methods, are also discussed. Key Messages: Slice-based representations are a common approach in radiation oncology, while volume visualization also has a long-standing history in the field. Perception within both representations can benefit further from advanced approaches, such as image fusion and multivolume or hybrid rendering. While traditional slice-based and volume representations keep evolving, the dimensionality and complexity of medical data are also increasing. To address this, visual analytics strategies are valuable, particularly for cohort or uncertainty visualization. Interactive visual analytics approaches represent a new opportunity to integrate knowledgeable experts and their cognitive abilities in exploratory processes which cannot be conducted by solely automatized methods.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1651, 563, 231, 1410 ], "doi": "https://doi.org/10.1159/000504940", "journal": "Oncology and Informatics", "open_access": "yes", "pages_from": "1", "pages_to": "11", "volume": "1", "research_areas": [], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_2020Onc-paper.pdf", "type": "application/pdf", "size": 900790, "path": "Publication:raidou_2020Onc", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_2020Onc/raidou_2020Onc-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_2020Onc/raidou_2020Onc-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_2020Onc/", "__class": "Publication" }, { "id": "amirkhanov-2019-manylands", "type_id": "journalpaper", "tu_id": 282817, "repositum_id": null, "title": "ManyLands: A Journey Across 4D Phase Space of Trajectories", "date": "2019-10", "abstract": "Mathematical models of ordinary differential equations are used to describe and understand biological phenomena. These models are dynamical systems that often describe the time evolution of more than three variables, i.e., their dynamics take place in a multi-dimensional space, called the phase space. Currently, mathematical domain scientists use plots of typical trajectories in the phase space to analyze the qualitative behavior of dynamical systems. These plots are called phase portraits and they perform well for 2D and 3D dynamical systems. However, for 4D, the visual exploration of trajectories becomes challenging, as simple subspace juxtaposition is not sufficient. We propose ManyLands to support mathematical domain scientists in analyzing 4D models of biological systems. By describing the subspaces as Lands, we accompany domain scientists along a continuous journey through 4D HyperLand, 3D SpaceLand, and 2D FlatLand, using seamless transitions. The Lands are also linked to 1D TimeLines. We offer an additional dissected view of trajectories that relies on small-multiple compass-alike pictograms for easy navigation across subspaces and trajectory segments of interest. We show three use cases of 4D dynamical systems from cell biology and biochemistry. An informal evaluation with mathematical experts confirmed that ManyLands helps them to visualize and analyze complex 4D dynamics, while facilitating mathematical experiments and simulations.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 4939, "image_height": 2158, "name": "amirkhanov-2019-manylands-Teaser.png", "type": "image/png", "size": 1836064, "path": "Publication:amirkhanov-2019-manylands", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1170, 1692, 418, 817, 869, 166, 1410 ], "cfp": { "name": "Pacific Graphics 2019 - call for papers.pdf", "type": "application/pdf", "error": "0", "size": "1632206", "orig_name": "Pacific Graphics 2019 - call for papers.pdf", "ext": "pdf" }, "date_from": "2019-01-01", "date_to": "2019-06-07", "doi": "10.1111/cgf.13828", "event": "Pacific Graphics 2019", "journal": "Computer Graphics Forum", "lecturer": [ 1170 ], "location": "Seoul, South Korea", "number": "7", "pages_from": "191", "pages_to": "202", "volume": "38", "research_areas": [ "BioVis" ], "keywords": [ "Visual analytics", "Web-based interaction" ], "weblinks": [ { "href": "https://amirkhanov.net/manylands", "caption": "ManyLands repository", "description": null, "main_file": 0 }, { "href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.13828", "caption": "E-print", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "amirkhanov-2019-manylands-Paper.pdf", "type": "application/pdf", "size": 2824013, "path": "Publication:amirkhanov-2019-manylands", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Paper:thumb{{size}}.png" }, { "description": null, "filetitle": "Teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 4939, "image_height": 2158, "name": "amirkhanov-2019-manylands-Teaser.png", "type": "image/png", "size": 1836064, "path": "Publication:amirkhanov-2019-manylands", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Teaser:thumb{{size}}.png" }, { "description": null, "filetitle": "Video demo", "main_file": true, "use_in_gallery": false, "access": "public", "name": "amirkhanov-2019-manylands-Video demo.mp4", "type": "video/mp4", "size": 116969399, "path": "Publication:amirkhanov-2019-manylands", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Video demo.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Video demo:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/amirkhanov-2019-manylands-Video demo:video.mp4" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/", "__class": "Publication" }, { "id": "grossmann_2019_pelvisrunner_poster", "type_id": "poster", "tu_id": null, "repositum_id": null, "title": "Pelvis Runner: A Visual Analytics Tool for Pelvic Organ Variability Exploration in Prostate Cancer Cohorts", "date": "2019-10", "abstract": "Pelvis Runner is a visual analysis tool for the exploration of the variability of segmented pelvic organs in multiple patients, across the course of radiation therapy treatment. Radiation treatment is performed through the course of weeks, during which the anatomy of the patient changes. This variability may be responsible for side effects, due to the potential over-irradiation of healthy tissues. Exploring and analyzing organ variability in patient cohorts can help clinical researchers to design more robust treatment strategies. Our work addresses, first, the global exploration and analysis of pelvic organ shape variability in an abstracted tabular view for the entire cohort. Second, local exploration and analysis of the variability are provided on-demand in anatomical 2D/3D views for cohort partitions. The Pelvis Runner has been evaluated by two clinical researchers and is a promising basis for the exploration of pelvic organ variability.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1366, 1444, 1445, 1568, 1569, 166, 1410 ], "cfp": { "name": "call_for_posters.pdf", "type": "application/pdf", "error": "0", "size": "179727", "orig_name": "call_for_posters.pdf", "ext": "pdf" }, "date_from": "2019-10-20", "date_to": "2019-10-25", "event": "IEEE VIS VAST", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "poster", "main_file": true, "use_in_gallery": false, "access": "public", "name": "grossmann_2019_pelvisrunner_poster-poster.pdf", "type": "application/pdf", "size": 1938168, "path": "Publication:grossmann_2019_pelvisrunner_poster", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/grossmann_2019_pelvisrunner_poster/grossmann_2019_pelvisrunner_poster-poster.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/grossmann_2019_pelvisrunner_poster/grossmann_2019_pelvisrunner_poster-poster:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/grossmann_2019_pelvisrunner_poster/", "__class": "Publication" }, { "id": "vitruvian_2019", "type_id": "journalpaper", "tu_id": 283956, "repositum_id": null, "title": "The Vitruvian Baby: Interactive Reformation of Fetal Ultrasound Data to a T-Position", "date": "2019-09", "abstract": "Three-dimensional (3D) ultrasound imaging and visualization is often used in medical diagnostics, especially in prenatal\nscreening. Screening the development of the fetus is important to assess possible complications early on. State of the art approaches involve taking standardized measurements to compare them with standardized tables. The measurements are taken\nin a 2D slice view, where precise measurements can be difficult to acquire due to the fetal pose. Performing the analysis in a\n3D view would enable the viewer to better discriminate between artefacts and representative information. Additionally making\ndata comparable between different investigations and patients is a goal in medical imaging techniques and is often achieved by\nstandardization. With this paper, we introduce a novel approach to provide a standardization method for 3D ultrasound fetus\nscreenings. Our approach is called “The Vitruvian Baby” and incorporates a complete pipeline for standardized measuring\nin fetal 3D ultrasound. The input of the method is a 3D ultrasound screening of a fetus and the output is the fetus in a standardized T-pose. In this pose, taking measurements is easier and comparison of different fetuses is possible. In addition to the\ntransformation of the 3D ultrasound data, we create an abstract representation of the fetus based on accurate measurements.\nWe demonstrate the accuracy of our approach on simulated data where the ground truth is known.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "name": "vitruvian_2019-.pdf", "type": "application/pdf", "size": 1371768, "path": "Publication:vitruvian_2019", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/vitruvian_2019/vitruvian_2019-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/vitruvian_2019/vitruvian_2019-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1345, 1410, 171, 1694 ], "cfp": { "name": "For submitters - VCBM 2019.pdf", "type": "application/pdf", "error": "0", "size": "340448", "orig_name": "For submitters - VCBM 2019.pdf", "ext": "pdf" }, "date_from": "2019", "date_to": "2019", "doi": "10.2312/vcbm.20191245", "event": "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", "journal": "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", "lecturer": [ 1345 ], "pages_from": "201", "pages_to": "205", "volume": "9", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "name": "vitruvian_2019-.pdf", "type": "application/pdf", "size": 1371768, "path": "Publication:vitruvian_2019", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/vitruvian_2019/vitruvian_2019-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/vitruvian_2019/vitruvian_2019-:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/vitruvian_2019/", "__class": "Publication" }, { "id": "raidou_2019_pelvisrunner", "type_id": "inproceedings", "tu_id": 283994, "repositum_id": null, "title": "Pelvis Runner: Visualizing Pelvic Organ Variability in a Cohort of Radiotherapy Patients", "date": "2019-09", "abstract": "In radiation therapy, anatomical changes in the patient might lead to deviations between the planned and delivered dose--including inadequate tumor coverage, and overradiation of healthy tissues. Exploring and analyzing anatomical changes throughout the entire treatment period can help clinical researchers to design appropriate treatment strategies, while identifying patients that are more prone to radiation-induced toxicity. We present the Pelvis Runner, a novel application for exploring the variability of segmented pelvic organs in multiple patients, across the entire radiation therapy treatment process. Our application addresses (i) the global exploration and analysis of pelvic organ shape variability in an abstracted tabular view and (ii) the local exploration and analysis thereof in anatomical 2D/3D views, where comparative and ensemble visualizations are integrated. The workflow is based on available retrospective cohort data, which incorporate segmentations of the bladder, the prostate, and the rectum through the entire radiation therapy process. The Pelvis Runner is applied to four usage scenarios, which were conducted with two clinical researchers, i.e., medical physicists. Our application provides clinical researchers with promising support in demonstrating the significance of treatment plan adaptation to anatomical changes.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1366, 1444, 1445, 1568, 1569, 166, 1410 ], "booktitle": "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", "cfp": { "name": "For submitters - VCBM 2019.pdf", "type": "application/pdf", "error": "0", "size": "340448", "orig_name": "For submitters - VCBM 2019.pdf", "ext": "pdf" }, "doi": "10.2312/vcbm.20191233", "event": "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", "lecturer": [ 1366 ], "pages_from": "69", "pages_to": "78", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 9269, "preview_image_height": 4095, "name": "raidou_2019_pelvisrunner-paper.pdf", "type": "application/pdf", "size": 2868471, "path": "Publication:raidou_2019_pelvisrunner", "preview_name": "raidou_2019_pelvisrunner-paper:preview.jpg", "preview_type": "image/jpeg", "preview_size": 1955471, "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_pelvisrunner/raidou_2019_pelvisrunner-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_pelvisrunner/raidou_2019_pelvisrunner-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_2019_pelvisrunner-video.mp4", "type": "video/mp4", "size": 35722536, "path": "Publication:raidou_2019_pelvisrunner", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_pelvisrunner/raidou_2019_pelvisrunner-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_pelvisrunner/raidou_2019_pelvisrunner-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_pelvisrunner/raidou_2019_pelvisrunner-video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_pelvisrunner/", "__class": "Publication" }, { "id": "raidou_2019_preha", "type_id": "inproceedings", "tu_id": 283963, "repositum_id": null, "title": "preha: Establishing Precision Rehabilitation with Visual Analytics", "date": "2019-09", "abstract": "This design study paper describes preha, a novel visual analytics application in the field of in-patient rehabilitation. We conducted extensive interviews with the intended users, i.e., engineers and clinical rehabilitation experts, to determine specific requirements of their analytical process.We identified nine tasks, for which suitable solutions have been designed and developed in the flexible environment of kibana. Our application is used to analyze existing rehabilitation data from a large cohort of 46,000 patients, and it is the first integrated solution of its kind. It incorporates functionalities for data preprocessing (profiling, wrangling and cleansing), storage, visualization, and predictive analysis on the basis of retrospective outcomes. A positive feedback from the first evaluation with domain experts indicates the usefulness of the newly proposed approach and represents a solid foundation for the introduction of visual analytics to the rehabilitation domain.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1654, 235, 166, 1410 ], "booktitle": "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", "cfp": { "name": "For submitters - VCBM 2019.pdf", "type": "application/pdf", "error": "0", "size": "340448", "orig_name": "For submitters - VCBM 2019.pdf", "ext": "pdf" }, "doi": "10.2312/vcbm.20191234", "event": "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", "lecturer": [ 1410 ], "pages_from": "79", "pages_to": "89", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 1280, "preview_image_height": 720, "name": "raidou_2019_preha-paper.pdf", "type": "application/pdf", "size": 4173646, "path": "Publication:raidou_2019_preha", "preview_name": "raidou_2019_preha-paper:preview.png", "preview_type": "image/png", "preview_size": 821513, "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/raidou_2019_preha-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/raidou_2019_preha-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "slides", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_2019_preha-slides.pdf", "type": "application/pdf", "size": 1577612, "path": "Publication:raidou_2019_preha", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/raidou_2019_preha-slides.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/raidou_2019_preha-slides:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_2019_preha-video.mp4", "type": "video/mp4", "size": 19827660, "path": "Publication:raidou_2019_preha", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/raidou_2019_preha-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/raidou_2019_preha-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/raidou_2019_preha-video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/", "__class": "Publication" }, { "id": "raidou_2019_springer", "type_id": "inbook", "tu_id": 283965, "repositum_id": null, "title": "Visual Analytics for the Representation, Exploration and Analysis of High-Dimensional, Multi-Faceted Medical Data", "date": "2019-07-17", "abstract": "Medicine is among research fields with a significant impact on\nhumans and their health. Already for decades, medicine has established\na tight coupling with the visualization domain, proving the importance\nof developing visualization techniques, designed exclusively for this\nresearch discipline. However, medical data is steadily increasing in\ncomplexity with the appearance of heterogeneous, multi-modal, multiparametric,\ncohort or population, as well as uncertain data. To deal with\nthis kind of complex data, the field of Visual Analytics has emerged.\nIn this chapter, we discuss the many dimensions and facets of medical\ndata. Based on this classification, we provide a general overview of\nstate-of-the-art visualization systems and solutions dealing with highdimensional,\nmulti-faceted data. Our particular focus will be on multimodal,\nmulti-parametric data, on data from cohort or population studies\nand on uncertain data, especially with respect to Visual Analytics\napplications for the representation, exploration, and analysis of highdimensional,\nmulti-faceted medical data.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410 ], "booktitle": "Biomedical Visualisation", "chapter": "10", "doi": "https://doi.org/10.1007/978-3-030-14227-8_10", "editor": "Springer", "note": "https://www.springer.com/gp/book/9783030142261", "pages_from": "137", "pages_to": "162", "publisher": "Springer", "volume": "2", "research_areas": [ "BioVis", "InfoVis", "MedVis" ], "keywords": [], "weblinks": [ { "href": "https://www.springer.com/gp/book/9783030142261", "caption": "link", "description": null, "main_file": 0 } ], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_springer/", "__class": "Publication" }, { "id": "raidou_star2019", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "State-of-the-Art Report: Visual Computing in Radiation Therapy Planning", "date": "2019-06", "abstract": "Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which\nrequires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of\nRT planning, providing solutions in all parts of the process—from imaging to delivery. Despite the significant technological\nadvancements of RT over the last decades, there are still many challenges to address. This survey provides an overview of\nthe compound planning process of RT, and of the ways that VC has supported RT in all its facets. The RT planning process\nis described to enable a basic understanding in the involved data, users and workflow steps. A systematic categorization and\nan extensive analysis of existing literature in the joint VC/RT research is presented, covering the entire planning process. The\nsurvey concludes with a discussion on lessons learnt, current status, open challenges, and future directions in VC/RT research.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": null, "preview_image_height": null, "name": "raidou_star2019-.pdf", "type": "application/pdf", "size": 8627472, "path": "Publication:raidou_star2019", "preview_name": "raidou_star2019-:preview.pdf", "preview_type": "application/pdf", "preview_size": 295636, "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_star2019/raidou_star2019-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_star2019/raidou_star2019-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1651, 1410, 1445, 563, 231 ], "cfp": { "name": "Eurovis 2019.pdf", "type": "application/pdf", "error": "0", "size": "81765", "orig_name": "Eurovis 2019.pdf", "ext": "pdf" }, "date_from": "2019", "date_to": "2019", "event": "EuroVis 2019", "journal": "Computer Graphics Forum", "lecturer": [ 1410 ], "number": "38", "pages_from": "753", "pages_to": "779", "volume": "3", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": null, "preview_image_height": null, "name": "raidou_star2019-.pdf", "type": "application/pdf", "size": 8627472, "path": "Publication:raidou_star2019", "preview_name": "raidou_star2019-:preview.pdf", "preview_type": "application/pdf", "preview_size": 295636, "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_star2019/raidou_star2019-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_star2019/raidou_star2019-:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_star2019/", "__class": "Publication" }, { "id": "raidou_RO2019", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "PO-0962 Bladder changes during first week of RT for prostate cancer determine the risk of urinary toxicity", "date": "2019-04", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1444, 1410, 1670, 1568, 1569, 1671, 1672, 1445 ], "booktitle": "Radiotherapy and Oncology", "cfp": { "name": "1-s2.0-S0167814019326374-main.pdf", "type": "application/pdf", "error": "0", "size": "91275", "orig_name": "1-s2.0-S0167814019326374-main.pdf", "ext": "pdf" }, "doi": "https://doi.org/10.1016/S0167-8140(19)31382-9", "event": "ESTRO 38", "lecturer": [ 1444 ], "pages_from": "S522", "pages_to": "S523", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [ { "href": "https://www.thegreenjournal.com/article/S0167-8140(19)31382-9/fulltext", "caption": "Link", "description": null, "main_file": 0 } ], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_RO2019/", "__class": "Publication" }, { "id": "raidou2019_prsps", "type_id": "journalpaper", "tu_id": 283349, "repositum_id": null, "title": "Relaxing Dense Scatter Plots with Pixel-Based Mappings", "date": "2019-03", "abstract": "Scatter plots are the most commonly employed technique for the visualization of bivariate data. Despite their versatility and expressiveness in showing data aspects, such as clusters, correlations, and outliers, scatter plots face a main problem. For large and dense data, the representation suffers from clutter due to overplotting. This is often partially solved with the use of density plots. Yet, data overlap may occur in certain regions of a scatter or density plot, while other regions may be partially, or even completely empty. Adequate pixel-based techniques can be employed for effectively filling the plotting space, giving an additional notion of the numerosity of data motifs or clusters. We propose the Pixel-Relaxed Scatter Plots, a new and simple variant, to improve the display of dense scatter plots, using pixel-based, space-filling mappings. Our Pixel-Relaxed Scatter Plots make better use of the plotting canvas, while avoiding data overplotting, and optimizing space coverage and insight in the presence and size of data motifs. We have employed different methods to map scatter plot points to pixels and to visually present this mapping. We demonstrate our approach on several synthetic and realistic datasets, and we discuss the suitability of our technique for different tasks. Our conducted user evaluation shows that our Pixel-Relaxed Scatter Plots can be a useful enhancement to traditional scatter plots.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 166, 1451 ], "cfp": { "name": "PacificVis 2019.pdf", "type": "application/pdf", "error": "0", "size": "380233", "orig_name": "PacificVis 2019.pdf", "ext": "pdf" }, "date_from": "2019-03-18", "date_to": "2019-03-18", "doi": "10.1109/TVCG.2019.2903956", "event": "PacificVis 2019", "journal": "IEEE Transactions on Visualization and Computer Graphics", "lecturer": [ 1410 ], "pages_from": "1", "pages_to": "12", "volume": "25", "research_areas": [ "InfoVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": true, "access": "public", "preview_image_width": 1548, "preview_image_height": 877, "name": "raidou2019_prsps-paper.pdf", "type": "application/pdf", "size": 5294590, "path": "Publication:raidou2019_prsps", "preview_name": "raidou2019_prsps-paper:preview.JPG", "preview_type": "image/jpeg", "preview_size": 274161, "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou2019_prsps/raidou2019_prsps-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou2019_prsps/raidou2019_prsps-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/raidou2019_prsps/", "__class": "Publication" }, { "id": "manylands_award", "type_id": "unknown", "tu_id": null, "repositum_id": null, "title": "EG VCBM 2019 Image Contest Award, people's choice—ManyLands: A Journey Across 4D Phase Space of Biological Systems", "date": "2019", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Image 2", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 11693, "image_height": 8163, "name": "manylands_award-Image 2.png", "type": "image/png", "size": 5379688, "path": "Publication:manylands_award", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/manylands_award/manylands_award-Image 2.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/manylands_award/manylands_award-Image 2:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1170, 1692, 418, 817, 869, 166, 1410 ], "research_areas": [ "BioVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Image 1", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 4939, "image_height": 2158, "name": "manylands_award-Image 1.png", "type": "image/png", "size": 1836064, "path": "Publication:manylands_award", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/manylands_award/manylands_award-Image 1.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/manylands_award/manylands_award-Image 1:thumb{{size}}.png" }, { "description": null, "filetitle": "Image 2", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 11693, "image_height": 8163, "name": "manylands_award-Image 2.png", "type": "image/png", "size": 5379688, "path": "Publication:manylands_award", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/manylands_award/manylands_award-Image 2.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/manylands_award/manylands_award-Image 2:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/manylands_award/", "__class": "Publication" }, { "id": "raidou2018visualflatter", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "VisualFlatter - Visual Analysis of Distortions in the Projection of Biomedical Structures", "date": "2018-09", "abstract": "Projections of complex anatomical or biological structures from 3D to 2D are often used by visualization and domain experts\nto facilitate inspection and understanding. Representing complex structures, such as organs or molecules, in a simpler 2D\nway often requires less interaction, while enabling comparability. However, the most commonly employed projection methods\nintroduce size or shape distortions, in the resulting 2D representations. While simple projections display known distortion\npatterns, more complex projection algorithms are not easily predictable.We propose the VisualFlatter, a visual analysis tool that\nenables visualization and domain experts to explore and analyze projection-induced distortions, in a structured way. Our tool\nprovides a way to identify projected regions with semantically relevant distortions and allows users to comparatively analyze\ndistortion outcomes, either from alternative projection methods or due to different setups through the projection pipeline. The\nuser is given the ability to improve the initial projection configuration, after comparing different setups. We demonstrate the\nfunctionality of our tool using four scenarios of 3D to 2D projections, conducted with the help of domain or visualization experts\nworking on different application fields. We also performed a wider evaluation with 13 participants, familiar with projections,\nto assess the usability and functionality of the Visual Flatter.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1366, 1367, 166, 1410 ], "cfp": { "name": "UI.PNG", "type": "image/png", "error": "0", "size": "739280", "orig_name": "UI.PNG", "ext": "PNG" }, "date_from": "2018", "date_to": "2018", "event": "Eurographics Workshop on Visual Computing for Biology and Medicine (2018)", "journal": "Eurographics Proceedings", "lecturer": [ 1410 ], "research_areas": [ "BioVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "demo", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou2018visualflatter-demo.mp4", "type": "video/mp4", "size": 64003568, "path": "Publication:raidou2018visualflatter", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/raidou2018visualflatter-demo.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/raidou2018visualflatter-demo:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/raidou2018visualflatter-demo:video.mp4" }, { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1920, "image_height": 972, "name": "raidou2018visualflatter-image.png", "type": "image/png", "size": 771897, "path": "Publication:raidou2018visualflatter", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/raidou2018visualflatter-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/raidou2018visualflatter-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou2018visualflatter-paper.pdf", "type": "application/pdf", "size": 4204706, "path": "Publication:raidou2018visualflatter", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/raidou2018visualflatter-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/raidou2018visualflatter-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/", "__class": "Publication" }, { "id": "rraidou_clinical", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Uncertainty evaluation of image-based tumour control probability models in radiotherapy of prostate cancer using a visual analytic tool", "date": "2018-01", "abstract": "Functional imaging techniques provide radiobiological information that can be included into tumour control probability (TCP) models to enable individualized outcome predictions in radiotherapy. However, functional imaging and the derived radiobiological information are influenced by uncertainties, translating into variations in individual TCP predictions. In this study we applied a previously developed analytical tool to quantify dose and TCP uncertainty bands when initial cell density is estimated from MRI-based apparent diffusion coefficient maps of eleven patients. TCP uncertainty bands of 16% were observed at patient level, while dose variations bands up to 8 Gy were found at voxel level for an iso-TCP approach.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1444, 1410, 1447, 459, 1445 ], "journal": "Physics and Imaging in Radiation Oncology", "number": "5", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [ { "href": "http://www.phiro.science/article/S2405-6316(17)30058-1/abstract", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": 512, "preview_image_height": 512, "name": "rraidou_clinical-paper.pdf", "type": "application/pdf", "size": 748527, "path": "Publication:rraidou_clinical", "preview_name": "rraidou_clinical-paper:preview.jpg", "preview_type": "image/jpeg", "preview_size": 37473, "url": "https://www.cg.tuwien.ac.at/research/publications/2018/rraidou_clinical/rraidou_clinical-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/rraidou_clinical/rraidou_clinical-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/rraidou_clinical/", "__class": "Publication" }, { "id": "raidou_bestphd", "type_id": "misc", "tu_id": null, "repositum_id": null, "title": "EuroVis Best PhD Award 2018—Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline", "date": "2018", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 719, "image_height": 997, "name": "raidou_bestphd-.jpg", "type": "image/jpeg", "size": 182727, "path": "Publication:raidou_bestphd", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_bestphd/raidou_bestphd-.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_bestphd/raidou_bestphd-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410 ], "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 719, "image_height": 997, "name": "raidou_bestphd-.jpg", "type": "image/jpeg", "size": 182727, "path": "Publication:raidou_bestphd", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_bestphd/raidou_bestphd-.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_bestphd/raidou_bestphd-:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_bestphd/", "__class": "Publication" }, { "id": "EuroVis2018-ShortPapers-Reiter", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Comparative Visual Analysis of Pelvic Organ Segmentations", "date": "2018", "abstract": "In prostate cancer treatment, automatic segmentations of the pelvic organs are often used as input to radiotherapy planning systems. However, natural anatomical variability of the involved organs is a common reason, for which segmentation algorithms fail, introducing errors in the radiotherapy treatment procedure, as well. Understanding how the shape and size of these organs affect the accuracy of segmentation is of major importance for developers of segmentation algorithms. However, current means of exploration and analysis provide limited insight. In this work, we discuss the design and implementation of a web-based framework, which enables easy exploration and detailed analysis of shape variability, and allows the intended users - i.e., segmentation experts - to generate hypotheses in relation to the performance of the involved algorithms. Our proposed approach was tested with segmentation meshes from a small cohort of 17 patients. Each mesh consists of four pelvic organs and two organ interfaces, which are labeled and have per-triangle correspondences. A usage scenario and an initial informal evaluation with a segmentation expert demonstrate that our framework allows the developers of the algorithms to quickly identify inaccurately segmented organs and to deliberate about the relation of variability to anatomical features and segmentation quality.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1479, 679, 166, 1410 ], "cfp": { "name": "Doc1.pdf", "type": "application/pdf", "error": "0", "size": "81198", "orig_name": "Doc1.pdf", "ext": "pdf" }, "date_from": "2018-06", "date_to": "2018-06", "doi": "10.2312/eurovisshort.20181075", "event": "EuroVis 2018", "journal": "Computer Graphics Forum", "pages": "037-041", "research_areas": [], "keywords": [], "weblinks": [ { "href": "https://diglib.eg.org/handle/10.2312/eurovisshort20181075", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "Honorable Mention ", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1745, "image_height": 1309, "name": "EuroVis2018-ShortPapers-Reiter-Honorable Mention .jpg", "type": "image/jpeg", "size": 133647, "path": "Publication:EuroVis2018-ShortPapers-Reiter", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/EuroVis2018-ShortPapers-Reiter-Honorable Mention .jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/EuroVis2018-ShortPapers-Reiter-Honorable Mention :thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": null, "preview_image_height": null, "name": "EuroVis2018-ShortPapers-Reiter-paper.pdf", "type": "application/pdf", "size": 986839, "path": "Publication:EuroVis2018-ShortPapers-Reiter", "preview_name": "EuroVis2018-ShortPapers-Reiter-paper:preview.pdf", "preview_type": "application/pdf", "preview_size": 296777, "url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/EuroVis2018-ShortPapers-Reiter-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/EuroVis2018-ShortPapers-Reiter-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "Video", "main_file": true, "use_in_gallery": true, "access": "public", "name": "EuroVis2018-ShortPapers-Reiter-Video.mp4", "type": "video/mp4", "size": 41963961, "path": "Publication:EuroVis2018-ShortPapers-Reiter", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/EuroVis2018-ShortPapers-Reiter-Video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/EuroVis2018-ShortPapers-Reiter-Video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/EuroVis2018-ShortPapers-Reiter-Video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/", "__class": "Publication" }, { "id": "EuroVis2018-ShortPapers-Karall", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "ChemoExplorer: A Dashboard for the Visual Analysis of ChemotherapyResponse in Breast Cancer Patients", "date": "2018", "abstract": "In breast cancer chemotherapy treatment, different alternative strategies can be employed. Clinical researchers working on the optimization of chemotherapy strategies need to analyze the progress of the treatment and to understand how different groups of patients respond to selected therapies. This is a challenging task, because of the multitude of imaging and non-imaging health record data involved. We, hereby, introduce a web-based dashboard that facilitates the comparison and analysis of publicly available breast cancer chemotherapy response data, consisting of a follow-up study of 63 patients. Each patient received one of two available therapeutic strategies and their treatment response was documented. Our dashboard provides an initial basis for clinical researchers working on chemotherapy optimization, to analyze the progress of treatment and to compare the response of (groups of) patients with distinct treatment characteristics. Our approach consists of multiple linked representations that provide interactive views on different aspects of the available imaging and non-imaging data. To illustrate the functionality of the ChemoExplorer, we conducted a usage scenario that shows the initial results of our work.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": true, "use_in_gallery": true, "access": "public", "name": "EuroVis2018-ShortPapers-Karall-.mp4", "type": "video/mp4", "size": 36056866, "path": "Publication:EuroVis2018-ShortPapers-Karall", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/EuroVis2018-ShortPapers-Karall-.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/EuroVis2018-ShortPapers-Karall-:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/EuroVis2018-ShortPapers-Karall-:video.mp4" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1480, 166, 1410 ], "date_from": "2018-06", "date_to": "2018-06", "doi": "10.2312/eurovisshort.20181077", "event": "EuroVis 2018", "journal": "Computer Graphics Forum", "pages": "049-053", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": true, "use_in_gallery": true, "access": "public", "name": "EuroVis2018-ShortPapers-Karall-.mp4", "type": "video/mp4", "size": 36056866, "path": "Publication:EuroVis2018-ShortPapers-Karall", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/EuroVis2018-ShortPapers-Karall-.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/EuroVis2018-ShortPapers-Karall-:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/EuroVis2018-ShortPapers-Karall-:video.mp4" }, { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": null, "preview_image_height": null, "name": "EuroVis2018-ShortPapers-Karall-Paper.pdf", "type": "application/pdf", "size": 1251937, "path": "Publication:EuroVis2018-ShortPapers-Karall", "preview_name": "EuroVis2018-ShortPapers-Karall-Paper:preview.pdf", "preview_type": "application/pdf", "preview_size": 787536, "url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/EuroVis2018-ShortPapers-Karall-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/EuroVis2018-ShortPapers-Karall-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/", "__class": "Publication" }, { "id": "raidou_2018_bladderrunner", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Bladder Runner: Visual Analytics for the Exploration of RT-Induced Bladder Toxicity in a Cohort Study", "date": "2018", "abstract": "We present the Bladder Runner, a novel tool to enable detailed visual exploration and analysis of the impact of bladder shape variation on the accuracy of dose delivery, during the course of prostate cancer radiotherapy (RT). Our tool enables the investigation of individual patients and cohorts through the entire treatment process, and it can give indications of RT-induced complications for the patient. In prostate cancer RT treatment, despite the design of an initial plan prior to dose administration, bladder toxicity remains very common. The main reason is that the dose is delivered in multiple fractions over a period of weeks, during which, the anatomical variation of the bladder - due to differences in urinary filling - causes deviations between planned and delivered doses. Clinical researchers want to correlate bladder shape variations to dose deviations and toxicity risk through cohort studies, to understand which specific bladder shape characteristics are more prone to side effects. This is currently done with Dose-Volume Histograms (DVHs), which provide limited, qualitative insight. The effect of bladder variation on dose delivery and the resulting toxicity cannot be currently examined with the DVHs. To address this need, we designed and implemented the Bladder Runner, which incorporates visualization strategies in a highly interactive environment with multiple linked views. Individual patients can be explored and analyzed through the entire treatment period, while inter-patient and temporal exploration, analysis and comparison are also supported. We demonstrate the applicability of our presented tool with a usage scenario, employing a dataset of 29 patients followed through the course of the treatment, across 13 time points. We conducted an evaluation with three clinical researchers working on the investigation of RT-induced bladder toxicity. All participants agreed that Bladder Runner provides better understanding and new opportunities for the exploration and analysis of the involved cohort data.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1444, 1170, 1568, 1445, 1569, 459, 166 ], "date_from": "2018-06", "date_to": "2018-06", "doi": "10.1111/cgf.13413", "event": "EuroVis 2018", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1410 ], "number": "3", "pages": "205-216", "pages_from": "205", "pages_to": "216", "volume": "37", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": 1739, "preview_image_height": 707, "name": "raidou_2018_bladderrunner-Paper.pdf", "type": "application/pdf", "size": 2076364, "path": "Publication:raidou_2018_bladderrunner", "preview_name": "raidou_2018_bladderrunner-Paper:preview.jpg", "preview_type": "image/jpeg", "preview_size": 247274, "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_2018_bladderrunner/raidou_2018_bladderrunner-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_2018_bladderrunner/raidou_2018_bladderrunner-Paper:thumb{{size}}.png" }, { "description": null, "filetitle": "Video", "main_file": true, "use_in_gallery": true, "access": "public", "name": "raidou_2018_bladderrunner-Video.mp4", "type": "video/mp4", "size": 69890898, "path": "Publication:raidou_2018_bladderrunner", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_2018_bladderrunner/raidou_2018_bladderrunner-Video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_2018_bladderrunner/raidou_2018_bladderrunner-Video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_2018_bladderrunner/raidou_2018_bladderrunner-Video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_2018_bladderrunner/", "__class": "Publication" }, { "id": "raidou2018_rv3", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Uncertainty Visualization: Recent Developments and Future Challenges inProstate Cancer Radiotherapy Planning", "date": "2018", "abstract": "Radiotherapy is one of the most common treatment strategy for prostate cancer. Prior to radiotherapy, a complex process consisting of several steps is employed to create an optimal treatment plan. However, all these steps include several sources of uncertainty, which can be detrimental for the successful outcome of the treatment. In this work, we present a number of strategies from the field of Visual Analytics that have been recently designed and implemented, for the visualization of data, processes and uncertainties at each step of the planning pipeline. We additionally document our opinion on topics that have not been yet addressed, and could be interesting directions for future work.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410 ], "booktitle": "EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3) 2018", "date_from": "2018", "date_to": "2018", "event": "EuroRV3", "journal": "EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3) 2018", "pages": "013-017", "research_areas": [], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "name": "raidou2018_rv3-Image.pdf", "type": "application/pdf", "size": 556976, "path": "Publication:raidou2018_rv3", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018_rv3/raidou2018_rv3-Image.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018_rv3/raidou2018_rv3-Image:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": null, "preview_image_height": null, "name": "raidou2018_rv3-Paper.pdf", "type": "application/pdf", "size": 663354, "path": "Publication:raidou2018_rv3", "preview_name": "raidou2018_rv3-Paper:preview.pdf", "preview_type": "application/pdf", "preview_size": 556976, "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018_rv3/raidou2018_rv3-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018_rv3/raidou2018_rv3-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018_rv3/", "__class": "Publication" }, { "id": "rraidou_EG17", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline", "date": "2017-04", "abstract": "Prostate cancer is one of the most frequently occurring types of cancer in males. It is often treated with radiation therapy,which aims at irradiating tumors with a high dose, while sparing the surrounding healthy tissues. In the course of the years,radiotherapy technology has undergone great advancements. However, tumors are not only different from each other, theyare also highly heterogeneous within, consisting of regions with distinct tissue characteristics, which should be treated withdifferent radiation doses. Tailoring radiotherapy planning to the specific needs and intra-tumor tissue characteristics of eachpatient is expected to lead to more effective treatment strategies. Currently, clinical research is moving towards this direction,but an understanding of the specific tumor characteristics of each patient, and the integration of all available knowledge into apersonalizable radiotherapy planning pipeline are still required. The present work describes solutions from the field of VisualAnalytics, which aim at incorporating the information from the distinct steps of the personalizable radiotherapy planningpipeline, along with eventual sources of uncertainty, into comprehensible visualizations. All proposed solutions are meantto increase the – up to now, limited – understanding and exploratory capabilities of clinical researchers. These approachescontribute towards the interactive exploration, visual analysis and understanding of the involved data and processes at differentsteps of the radiotherapy planning pipeline, creating a fertile ground for future research in radiotherapy planning.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image2", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 273, "image_height": 142, "name": "rraidou_EG17-image2.JPG", "type": "image/jpeg", "size": 16034, "path": "Publication:rraidou_EG17", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-image2.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-image2:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 679, 459 ], "booktitle": "Computer Graphics Forum (Proceedings of Eurographics)", "journal": "Computer Graphics Forum (Proceedings of Eurographics)", "lecturer": [ 161, 951 ], "volume": "36", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image2", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 273, "image_height": 142, "name": "rraidou_EG17-image2.JPG", "type": "image/jpeg", "size": 16034, "path": "Publication:rraidou_EG17", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-image2.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-image2:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 198, "preview_image_height": 35, "name": "rraidou_EG17-Paper.pdf", "type": "application/pdf", "size": 171052, "path": "Publication:rraidou_EG17", "preview_name": "rraidou_EG17-Paper:preview.png", "preview_type": "image/png", "preview_size": 11846, "url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-Paper:thumb{{size}}.png" }, { "description": null, "filetitle": "Video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "rraidou_EG17-Video.mp4", "type": "video/mp4", "size": 291963837, "path": "Publication:rraidou_EG17", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-Video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-Video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/rraidou_EG17-Video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/", "__class": "Publication" }, { "id": "rraidou_phdbook", "type_id": "book", "tu_id": null, "repositum_id": null, "title": "Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline.", "date": "2017-03-29", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "name": "rraidou_phdbook-.pdf", "type": "application/pdf", "size": 22111641, "path": "Publication:rraidou_phdbook", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_phdbook/rraidou_phdbook-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_phdbook/rraidou_phdbook-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410 ], "isbn": "ISBN987-90-386-4230-7", "open_access": "yes", "pages": "1-240", "publisher": "TU Eindhoven", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [ { "href": "http://repository.tue.nl/858251", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "name": "rraidou_phdbook-.pdf", "type": "application/pdf", "size": 22111641, "path": "Publication:rraidou_phdbook", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_phdbook/rraidou_phdbook-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_phdbook/rraidou_phdbook-:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_phdbook/", "__class": "Publication" }, { "id": "Groeller_2016_P4", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Visual Analytics for the Exploration and Assessment of Segmentation Errors", "date": "2016-09-07", "abstract": "Several diagnostic and treatment procedures require the segmentation of anatomical structures from medical images. However, the automatic model-based methods that are often employed, may produce inaccurate segmentations. These, if used as input for diagnosis or treatment, can have detrimental effects for the patients. Currently, an analysis to predict which anatomic regions are more prone to inaccuracies, and to determine how to improve segmentation algorithms, cannot be performed. We propose a visual tool to enable experts, working on model-based segmentation algorithms, to explore and analyze the outcomes and errors of their methods. Our approach supports the exploration of errors in a cohort of pelvic organ segmentations, where the\nperformance of an algorithm can be assessed. Also, it enables the detailed exploration and assessment of segmentation errors, in individual subjects. To the best of our knowledge, there is no other tool with comparable functionality. A usage scenario is employed to explore and illustrate the capabilities of our visual tool. To further assess the value of the proposed tool, we performed an evaluation with five segmentation experts. The evaluation participants confirmed the potential of the tool in providing new insight into their data and employed algorithms. They also gave feedback for future improvements.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 360, "image_height": 335, "name": "Groeller_2016_P4-image.PNG", "type": "image/png", "size": 191339, "path": "Publication:Groeller_2016_P4", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P4/Groeller_2016_P4-image.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P4/Groeller_2016_P4-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1411, 679, 166, 459, 1412 ], "journal": "Eurographics Workshop on Visual Computing for Biology and Medicine", "lecturer": [ 161, 563, 459 ], "pages_from": "193", "pages_to": "202", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 360, "image_height": 335, "name": "Groeller_2016_P4-image.PNG", "type": "image/png", "size": 191339, "path": "Publication:Groeller_2016_P4", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P4/Groeller_2016_P4-image.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P4/Groeller_2016_P4-image:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Groeller_2016_P4-Paper.pdf", "type": "application/pdf", "size": 8104018, "path": "Publication:Groeller_2016_P4", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P4/Groeller_2016_P4-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P4/Groeller_2016_P4-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P4/", "__class": "Publication" }, { "id": "malan_fluoro", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "A fluoroscopy-based planning and guidance software tool for minimally invasive hip refixation by cement injection.", "date": "2016", "abstract": "PURPOSE:\nIn orthopaedics, minimally invasive injection of bone cement is an established technique. We present HipRFX, a software tool for planning and guiding a cement injection procedure for stabilizing a loosening hip prosthesis. HipRFX works by analysing a pre-operative CT and intraoperative C-arm fluoroscopic images.\nMETHODS:\nHipRFX simulates the intraoperative fluoroscopic views that a surgeon would see on a display panel. Structures are rendered by modelling their X-ray attenuation. These are then compared to actual fluoroscopic images which allow cement volumes to be estimated. Five human cadaver legs were used to validate the software in conjunction with real percutaneous cement injection into artificially created periprothetic lesions.\nRESULTS:\nBased on intraoperatively obtained fluoroscopic images, our software was able to estimate the cement volume that reached the pre-operatively planned targets. The actual median target lesion volume was 3.58 ml (range 3.17-4.64 ml). The median error in computed cement filling, as a percentage of target volume, was 5.3% (range 2.2-14.8%). Cement filling was between 17.6 and 55.4% (median 51.8%).\nCONCLUSIONS:\nAs a proof of concept, HipRFX was capable of simulating intraoperative fluoroscopic C-arm images. Furthermore, it provided estimates of the fraction of injected cement deposited at its intended target location, as opposed to cement that leaked away. This level of knowledge is usually unavailable to the surgeon viewing a fluoroscopic image and may aid in evaluating the success of a percutaneous cement injection intervention.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1437, 1438, 1410, 1439, 1440, 1441, 1442, 1443 ], "journal": "International journal of computer assisted radiology and surgery,", "number": "2", "pages_from": "281", "pages_to": "296", "volume": "11", "research_areas": [ "MedVis" ], "keywords": [], "weblinks": [ { "href": "https://www.ncbi.nlm.nih.gov/pubmed/26259554", "caption": "Paper can be accessed here", "description": null, "main_file": 0 } ], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/malan_fluoro/", "__class": "Publication" }, { "id": "raidou_miccai16", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Employing Visual Analytics to Aid the Design of White Matter Hyperintensity Classifiers.", "date": "2016", "abstract": "Accurate segmentation of brain white matter hyperintensi-ties (WMHs) is important for prognosis and disease monitoring. To thisend, classi\fers are often trained { usually, using T1 and FLAIR weightedMR images. Incorporating additional features, derived from di\u000busionweighted MRI, could improve classi\fcation. However, the multitude ofdi\u000busion-derived features requires selecting the most adequate. For this,automated feature selection is commonly employed, which can often besub-optimal. In this work, we propose a di\u000berent approach, introducing asemi-automated pipeline to select interactively features for WMH classi\f-cation. The advantage of this solution is the integration of the knowledgeand skills of experts in the process. In our pipeline, a Visual Analytics(VA) system is employed, to enable user-driven feature selection. Theresulting features are T1, FLAIR, Mean Di\u000busivity (MD), and RadialDi\u000busivity (RD) { and secondarily,CSand Fractional Anisotropy (FA).The next step in the pipeline is to train a classi\fer with these features,and compare its results to a similar classi\fer, used in previous work withautomated feature selection. Finally, VA is employed again, to analyzeand understand the classi\fer performance and results.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1433, 1434, 1435, 1436, 679, 459 ], "journal": "Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "demo", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_miccai16-demo.mp4", "type": "video/mp4", "size": 40385545, "path": "Publication:raidou_miccai16", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_miccai16/raidou_miccai16-demo.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_miccai16/raidou_miccai16-demo:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_miccai16/raidou_miccai16-demo:video.mp4" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_miccai16-paper.pdf", "type": "application/pdf", "size": 927133, "path": "Publication:raidou_miccai16", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_miccai16/raidou_miccai16-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_miccai16/raidou_miccai16-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_miccai16/", "__class": "Publication" }, { "id": "raidou_eurovis16", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response.", "date": "2016", "abstract": "In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the prob-ability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research.These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional informationfrom imaging modalities. In this way, patient-specific properties of tumor tissues are included, improving the radiobiologicalaccuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modelingoutcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivityare not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enablesclinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledgeand to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1)It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to com-mon assumptions; (3) It enables the identification of inter-patient response variability; (4) It allows starting the analysis fromthe desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinicalresearchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for theexploration and analysis of TCP modeling.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_eurovis16-.pdf", "type": "application/pdf", "size": 2982085, "path": "Publication:raidou_eurovis16", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/raidou_eurovis16-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/raidou_eurovis16-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1444, 1445, 1446, 1447, 679, 459 ], "journal": "EuroVis - Eurographics/IEEE-VGTC Symposium on Visualization 2016", "lecturer": [ 1448, 1449, 652 ], "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_eurovis16-.pdf", "type": "application/pdf", "size": 2982085, "path": "Publication:raidou_eurovis16", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/raidou_eurovis16-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/raidou_eurovis16-:thumb{{size}}.png" }, { "description": null, "filetitle": "demo", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_eurovis16-demo.mp4", "type": "video/mp4", "size": 43216314, "path": "Publication:raidou_eurovis16", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/raidou_eurovis16-demo.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/raidou_eurovis16-demo:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/raidou_eurovis16-demo:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/", "__class": "Publication" }, { "id": "raidou_vis15", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Orientation-Enhanced Parallel Coordinate Plots", "date": "2015", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1451, 679, 823, 459 ], "journal": "IEEE transactions on visualization and computer graphics", "lecturer": [ 1452, 1453, 1448 ], "number": "1", "pages_from": "589", "pages_to": "598", "volume": "22", "research_areas": [ "InfoVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_vis15-paper.pdf", "type": "application/pdf", "size": 3927905, "path": "Publication:raidou_vis15", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_vis15/raidou_vis15-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_vis15/raidou_vis15-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_vis15/", "__class": "Publication" }, { "id": "raidou_EuroVis15", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Visual analytics for the exploration of tumor tissue characterization", "date": "2015", "abstract": "Tumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization ofthese intra-tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissuecharacterization could be performed non-invasively, using medical imaging data, to derive per voxel a number offeatures, indicative of tissue properties. However, the high dimensionality and complexity of this imaging-derivedfeature space is prohibiting for easy exploration and analysis - especially when clinical researchers require toassociate observations from the feature space to other reference data, e.g., features derived from histopathologicaldata. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visuallycomparing them and mentally reconstructing their relationships. This is a time consuming and tedious process,from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visualanalysis of the feature space of imaging-derived tissue characteristics and (2) knowledge discovery and hypothesisgeneration and confirmation, with respect to reference data used in clinical research. We employ, as central view,a 2D embedding of the imaging-derived features. Multiple linked interactive views provide functionality for theexploration and analysis of the local structure of the feature space, enabling linking to patient anatomy andclinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreedthat, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneousintra-tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1446, 1454, 1455, 1456, 679, 459 ], "journal": "In Computer Graphics Forum", "lecturer": [ 1460, 1448, 1449 ], "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_EuroVis15-paper.pdf", "type": "application/pdf", "size": 2846498, "path": "Publication:raidou_EuroVis15", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_EuroVis15/raidou_EuroVis15-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_EuroVis15/raidou_EuroVis15-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_EuroVis15-video.mp4", "type": "video/mp4", "size": 30229493, "path": "Publication:raidou_EuroVis15", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_EuroVis15/raidou_EuroVis15-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_EuroVis15/raidou_EuroVis15-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_EuroVis15/raidou_EuroVis15-video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_EuroVis15/", "__class": "Publication" }, { "id": "raidou_vis14", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Visual analytics for the exploration of multiparametric cancer imaging", "date": "2014", "abstract": "Tumor tissue characterization can play an important role in thediagnosis and design of effective treatment strategies. In orderto gather and combine the necessary tissue information, multi-modal imaging is used to derive a number of parameters indica-tive of tissue properties. The exploration and analysis of relation-ships between parameters and, especially, of differences among dis-tinct intra-tumor regions is particularly interesting for clinical re-searchers to individualize tumor treatment. However, due to highdata dimensionality and complexity, the current clinical workflowis time demanding and does not provide the necessary intra-tumorinsight. We implemented a new application for the exploration ofthe relationships between parameters and heterogeneity within tu-mors. In our approach, we employ a well-known dimensionalityreduction technique [5] to map the high-dimensional space of tis-sue properties into a 2D information space that can be interactivelyexplored with integrated information visualization techniques. Weconducted several usage scenarios with real-patient data, of whichwe present a case of advanced cervical cancer. First indicationsshow that our application introduces new features and functionali-ties that are not available within the current clinical approach.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1457, 1458, 679, 459 ], "journal": "In Visual Analytics Science and Technology (VAST), 2014 IEEE Conference on Visualization", "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_vis14-paper.pdf", "type": "application/pdf", "size": 492922, "path": "Publication:raidou_vis14", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vis14/raidou_vis14-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vis14/raidou_vis14-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vis14/", "__class": "Publication" }, { "id": "raidou_vcbm14", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "The iCoCooN:Integration of Cobweb Charts with Parallel Coordinates forVisual Analysis of DCE-MRI Modeling Variations", "date": "2014", "abstract": "Efficacy of radiotherapy treatment depends on the specific characteristics of tumorous tissues. For the determi-nation of these characteristics, clinical practice uses Dynamic Contrast Enhanced (DCE) Magnetic ResonanceImaging (MRI). DCE-MRI data is acquired and modeled using pharmacokinetic modeling, to derive per voxela set of parameters, indicative of tissue properties. Different pharmacokinetic modeling approaches make differ-ent assumptions, resulting in parameters with different distributions. A priori, it is not known whether there aresignificant differences between modeling assumptions and which assumption is best to apply. Therefore, clinicalresearchers need to know at least how different choices in modeling affect the resulting pharmacokinetic parame-ters and also where parameter variations appear. In this paper, we introduce iCoCooN: a visualization applicationfor the exploration and analysis of model-induced variations in pharmacokinetic parameters. We designed a visualrepresentation, the Cocoon, by integrating perpendicularly Parallel Coordinate Plots (PCPs) with Cobweb Charts(CCs). PCPs display the variations in each parameter between modeling choices, while CCs present the relationsin a whole parameter set for each modeling choice. The Cocoon is equipped with interactive features to supportthe exploration of all data aspects in a single combined view. Additionally, interactive brushing allows to link theobservations from the Cocoon to the anatomy. We conducted evaluations with experts and also general users. Theclinical experts judged that the Cocoon in combination with its features facilitates the exploration of all significantinformation and, especially, enables them to find anatomical correspondences. The results of the evaluation withgeneral users indicate that the Cocoon produces more accurate results compared to independent multiples", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_vcbm14-.mp4", "type": "video/mp4", "size": 44770304, "path": "Publication:raidou_vcbm14", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/raidou_vcbm14-.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/raidou_vcbm14-:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/raidou_vcbm14-:video.mp4" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1446, 1459, 679, 459 ], "journal": "Eurographics Workshop on Visual Computing for Biology and Medicine ", "lecturer": [ 171, 231, 951 ], "research_areas": [ "InfoVis", "MedVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_vcbm14-.mp4", "type": "video/mp4", "size": 44770304, "path": "Publication:raidou_vcbm14", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/raidou_vcbm14-.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/raidou_vcbm14-:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/raidou_vcbm14-:video.mp4" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "raidou_vcbm14-paper.pdf", "type": "application/pdf", "size": 2017346, "path": "Publication:raidou_vcbm14", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/raidou_vcbm14-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/raidou_vcbm14-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/", "__class": "Publication" }, { "id": "wu-2021", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Visualization working group at TU Wien: Visible Facimus Quod Ceteri Non Possunt", "date": null, "abstract": "Building-up and running a university-based research group is a multi-faceted undertaking. The visualization working group at TU Wien (vis-group) has been internationally active over more than 25 years. The group has been acting in a competitive scientific setting where sometimes contradicting multiple objectives require trade-offs and optimizations. Research-wise the group has been performing basic and applied research in visualization and visual computing. Teaching-wise the group has been involved in undergraduate and graduate lecturing in (medical) visualization and computer graphics. To be scientifically competitive requires to constantly expose the group and its members to a strong international competition at the highest level. This necessitates to shield the members against the ensuing pressures and demands and provide (emotional) support and encouragement. Internally, the vis-group has developed a unique professional and social interaction culture: work and celebrate, hard and together. This has crystallized into a nested, recursive, and triangular organization model, which concretizes what it takes to make a research group successful. The key elements are the creative and competent vis-group members who collaboratively strive for (scientific) excellence in a socially enjoyable environment.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "name": "wu-2021-image.png", "type": "image/png", "size": null, "path": "Publication:wu-2021", "url": "https://www.cg.tuwien.ac.at/research/publications/ongoing/wu-2021/wu-2021-image.png", "thumb_image_sizes": [] }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1464, 1170, 1366, 1285, 1383, 1263, 1577, 935, 1410, 171, 1110, 166 ], "doi": "https://doi.org/10.1016/j.visinf.2021.02.003", "journal": "Visual Informatics", "open_access": "yes", "pages_from": "76", "pages_to": "84", "volume": "5", "research_areas": [], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "name": "wu-2021-image.png", "type": "image/png", "size": null, "path": "Publication:wu-2021", "url": "https://www.cg.tuwien.ac.at/research/publications/ongoing/wu-2021/wu-2021-image.png", "thumb_image_sizes": [] }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "wu-2021-paper.pdf", "type": "application/pdf", "size": null, "path": "Publication:wu-2021", "url": "https://www.cg.tuwien.ac.at/research/publications/ongoing/wu-2021/wu-2021-paper.pdf", "thumb_image_sizes": [] } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/ongoing/wu-2021/", "__class": "Publication" } ]