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"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.",
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"abstract": "Traditional machine learning approaches for analyzing large unstructured data often depend on labelled training data and well-defined target definitions. However, these may not be available or feasible when dealing with unknown and unstructured data. It requires human reasoning and domain knowledge to interpret it. Interactive systems that combine human analytical abilities with machine learning techniques can address this limitation. However, to incorporate human knowledge in such systems, we need a better understanding of the semantic information and structures that users observe and expect while exploring unstructured data, as well as how they make their tacit knowledge explicit. This thesis aims to narrow the gap between human cognition and (knowledge-assisted) visual analytics. In a qualitative and exploratory user study, this thesis investigates how individuals explore a large unstructured dataset and which strategies they apply to externalize their mental models. By analyzing users' externalized mental models, we aim to better understand how their knowledge evolves during data exploration. We evaluate the comprehensiveness, detail and evolution of users' external knowledge representations by applying quantitative and qualitative methods, including a crowdsourcing step. The results show that users' externalized structures are able to represent a given dataset comprehensively and to a high degree of detail. While these knowledge representations are highly subjective and show various individual differences, we could identify structural similarities between individuals. In addition to the insights about how users externalize their tacit knowledge during data exploration, we propose design guidelines for (knowledge-assisted) visual analytics systems.",
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"title": "Addressing Data Heterogeneity in Image-Based Computer-Aided Detection and Diagnosis Methods",
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"abstract": "The acquisition of medical imaging data is inevitable for screening, diagnosis, planning of surgery or therapy, or monitoring of diseases. In clinical practice, the data is assessed by medical experts, which can be a very time-consuming task. Hence, for decades a lot of research effort has been dedicated to the automated analysis of medical imaging data and to the question of how Computer-Aided Detection and Diagnosis algorithms can assist the tasks mentioned above. However, one of the biggest challenges in this regard is the highly heterogeneous nature of medical imaging data. The acquisition of data from different imaging modalities, like X-ray or Magnetic Resonance Imaging (MRI), changes of acquisition parameters, and the use of different scanners results in diverse data. The varying spatial resolution as well as the high dimensionality of the data pose additional challenges to the development of automated solutions. In this thesis, we investigate different machine learning-based methods to address the analysis of heterogeneous medical imaging data, such as multi-parametric, multi-modal, multi-center, or multi-view data. We present three different pipeline approaches that follow generalization- and fusion- based approaches and demonstrate their applicability on diverse public datasets. Our contributions target two selected use cases in radiology: the semantic labeling of the spine in MRI data and the analysis of mammograms. In semi- and fully-automated spine labeling in MRI data, we are confronted with the problem that MRI data does not exhibit a standardized intensity scale, which results in a large variety of different image contrasts. To overcome this problem for semantic spine labeling, we propose an iterative labeling pipeline that employs Entropy-Optimized Texture Models (ETMs). The application of trained ETMs allows us to apply our models to a wide range of different MRI data. This is in contrast to various related works that develop methods for specific MRI image sequences and protocols. For the analysis of mammography screening data, not only one but four X-ray images from different fields of view are available that form a study of a patient. In addition to this multi-view data, we deal with multi-scale information at various levels, e.g., on a patient, image, or lesion level. To utilize and combine this information efficiently, we develop several deep learning-based models that aim for a specific task important in examining mammograms, such as the localization of abnormalities. For a comprehensive prediction on a patient level, we propose to fuse predictions and features from the individual models to increase performance, which is in contrast to standard ensembling techniques. The results in this thesis demonstrate that considering the different aspects of heterogeneous medical imaging data is inevitable to improve both generalization and predictive capabilities of Computer-Aided Detection and Diagnosis methods.",
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"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.",
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"number": "7",
"pages": "13",
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"volume": "43",
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"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.",
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"booktitle": "Healthcare Technology Letters",
"date_from": "2024-10-06",
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"doi": "10.1049/htl2.12094",
"event": "27th International Conference on Medical Image Computing and Computer Assisted Invertention (MICCAI 2024)",
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"title": "Proceedings of the 28th Central European Seminar on Computer Graphics : CESCG 2024",
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"doi": "10.34726/8401",
"editor": "Ilčík, Martin and Bittner, Jiří and Berger Haladová, Zuzana and Wimmer, Michael",
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"id": "toepfer-2024-spx",
"type_id": "masterthesis",
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"repositum_id": "20.500.12708/203924",
"title": "SPX: A Versatile Spatial Indexing Framework",
"date": "2024",
"abstract": "The integration of modern genetic techniques, advanced volumetric imaging methods, and single-cell extraction methods has empowered neurobiologists to create extensive digitized and coregistered specimen sample collections. These collections serve as valuable resources for studying neuronal structures, functional compartments, and neurological connections within the brain. By sampling single cells from various locations within the animal brain, scientists can investigate cell type distributions and gene expressions. However, the exploration of these vast collections, which include volumetric images, segmented structures, and gene expression data, poses a significant challenge in neuroscience. Efficient access to specific regions of interest in all images, derivative processed data, cell samples, and metadata is essential for researchers. In this thesis, I present a flexible and extensible approach to spatially index and store volumetric grid data and region-based data, enabling efficient access and providing a streamlined method for implementing new data abstractions, query types, and indexing strategies. The framework supports different datasets, the encoding of neurological structural types, and incorporates a layering mechanism to handle multiple data representations or time-dependent data within a single data structure. Standardized interfaces are defined for loading voxel and region data, preprocessing them, creating data abstractions, and implementing new query types. The data storage is managed using a storage engine approach, allowing users to leverage different storage mechanisms or introduce their own.This thesis provides an overview of conceptual ideas, implementation details, current data abstractions, and query types. The system was evaluated in terms of performance and scalability in its current use cases. A short introduction to three applications, BrainBaseWeb, BrainTrawler, and BrainTrawler Lite, exemplifies the usage of this framework.",
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"id": "matt-2024-cvi",
"type_id": "masterthesis",
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"repositum_id": "20.500.12708/198406",
"title": "Class-Centric Visual Interactive Labeling using Property Measures",
"date": "2024",
"abstract": "Human annotation of image data is relevant for supervised machine learning, where labeled datasets are essential for training models. Traditionally, reducing the labeling effort was achieved through active learning, where the optimal next instance for labeling is selected by some heuristic to maximize utility. More recent work has focused on integrating user initiative in the labeling process through visual interactive labeling to steer the labeling process. This thesis proposes cVIL, a class-centric approach for visual interactive labeling that simplifies the human annotation process for large and complex image datasets. Previously, visual labeling approaches were typically instance-based, where the system visualizes individual instances for the user to label. cVIL utilizes diverse property measures to enable the labeling of difficult instances individually and in batches to label simpler cases rapidly. Since the property measures express the properties of an instance using a single scalar value, the visualizations are simple and scalable. cVIL combines the heuristic guidance approach of active learning with the user-centered approach of visual interactive labeling. In simulations, we could show that property measures can facilitate effective instance and batch labeling. In a user study, cVIL demonstrated superior accuracy and user satisfaction compared to the conventional instance-based visual interactive labeling approach that employs scatterplots. Participants also needed less time to complete the assigned tasks in cVIL compared to the baseline.",
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"doi": "10.34726/hss.2024.102653",
"open_access": "yes",
"pages": "101",
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"id": "riegelnegg-2024-aeo",
"type_id": "masterthesis",
"tu_id": null,
"repositum_id": "20.500.12708/198102",
"title": "Automated Extraction of Complexity Measures from Engineering Drawings",
"date": "2024",
"abstract": "An engineering drawing is a detailed representation of an object used to communicate complex information for the purposes of design, manufacturing, and maintenance.These line drawings typically consist of multiple 2D orthographic views of a 3D object, along with dimensioning information and metadata about specific properties.Over the past decades, engineering drawings have evolved from hand-drawn sketches to highly standardized documents created with the help of CAD software.The large variety of engineering drawings makes it difficult to automatically extract abstract information in a robust way.The emergence of additive manufacturing (AM) promises companies that they can produce spare parts on demand for maintenance, potentially increasing the operational time of their infrastructure.Evaluating the AM potential of spare parts is essential from both an economic and technical perspective.This analysis of economic and technical viability requires the interpretation of complexity measures that can be derived from the engineering drawing of a spare part.The external dimensions of an object are key complexity measures to facilitate an AM potential analysis.In this thesis, we propose a processing pipeline that automates the extraction of complexity measures from engineering drawings, focusing on the external dimensions of the depicted objects.An in-depth examination of engineering drawings from different eras forms the basis of our methodology.Our pipeline is designed to be adaptable and consists of interpretable stages for specific tasks.We segment important entities in the input drawing to detect candidate dimension lines that are subsequently filtered by a sequence of processing steps.The grid structure of the orthographic views is determined, which allows us to assign axis labels to each view.We run optical character recognition (OCR) on detected dimension numbers and use the results to optimize the ratio between the OCR values and the length of dimension lines in pixels, providing us with a solution that is resilient to errors in the OCR predictions.A prototypical implementation of our pipeline demonstrates its capabilities in handling a large variety of drawings.We conduct a basic quantitative and qualitative evaluation of our methodology.The results confirm the effectiveness of our approach in automatically extracting abstract information from real-world engineering drawings.",
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"pattern recognition",
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"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.",
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"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)",
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"location": "Coimbra",
"pages": "6",
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"Meteorological Information",
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"Image color analysis",
"Data visualization",
"Rivers",
"Space stations",
"Bars"
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"id": "boffi-2024-bagginghook",
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"repositum_id": "20.500.12708/197699",
"title": "BaggingHook: Selecting Moving Targets by Pruning Distractors Away for Intention-Prediction Heuristics in Dense 3D Environments",
"date": "2024",
"abstract": "Selecting targets in dense, dynamic 3D environments presents a significant challenge. In this study, we introduce two novel selection techniques based on distractor pruning to assist users in selecting targets moving unpredictably: BaggingHook and AutoBaggingHook. Both are built upon the Hook intention-prediction heuristic, which continuously measures the distance between the user's cursor and each object to compute per-object scores and estimate the intended target. Our techniques reduce the number of targets in the environment, making heuristic convergence potentially faster. Once pruned away, distractors are also made semi-transparent to reduce occlusion and the overall difficulty of the task. However, their motion is not altered, so that users can still perceive the dynamics of the environment. We designed two pruning approaches: BaggingHook lets users manually prune distractors away, while AutoBaggingHook uses automated, score-based pruning. We conducted a user study in a virtual reality setting inspired by molecular dynamics simulations, featuring crowded scenes of objects moving fast and unpredictably, in 3D. We compared both proposed techniques to the Hook baseline under more challenging circumstances than it had previously been tested. Our results show that AutoBaggingHook was the fastest, and did not lead to higher error rates. BaggingHook, on the other hand, was preferred by the majority of participants, due to the greater degree of control it provides to users, leading some to see entertainment value in its use. This work shows the potential benefits of varying the types of inputs used in intention-prediction heuristics, not just to improve performance, but also to reduce occlusion, overall task load, and improve user experience.",
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"booktitle": "2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)",
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"title": "Alpine Terrain Relighting",
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"abstract": "Aerial orthophotos together with digital elevation models (DEMs) allow the rendering of 3D representations of the earth, including alpine terrain. These virtual landscapes provide the opportunity to simulate light conditions at different times of the day, aiding in trip planning. However, orthophotos used as texture often contain large shadows stemming from cliffs and rocks, which significantly impact the visual quality of relighted textures. The necessary single-image shadow-removal process presents a crucial problem for the computer vision domain, which also functions as a prerequisite for many other tasks like segmentation and classification. Many promising approaches have already been developed, but unlike previous methods, this study tries to capitalize on the availability of DEMs to enhance the shadow removal process. Shadows in orthophotos are inherently linked to the underlying geospatial topology, and DEMs provide a valuable source of information for mitigating their impact. Therefore, this thesis explores the integration of DEMs into a state-of-the-art deep learning pipeline. DEMs are examined for their role in generating training sets and as supplementary input for a multi-modal network. Notably, 3D geometry derived from DEMs complemented by ray-tracing is used to generate artificial shadows with realistic shapes. Subsequently, an experiment is conducted with the created dataset to empirically test if additional elevation data is beneficial for the performance of the models. Additionally, the model’s ability to generalize from artificial to real shadows was probed. The experiment on virtual shadows showed that providing additional elevation data to the shadow-removal network does yield significantly better results with a medium to large effect size. Initially, all trained models failed to generalize to real shadow data. Downsizing the dataset to a lower level of detail mitigated this problem. Together with an analysis of the output of each network layer, it was concluded that the reason for the subpar real data performance are remaining small-scale shadows in the train set. A visual analysis of the improved models showed noticeable improvements with the generated realistic shadow shapes compared to random ones. Moreover, the utility of additional elevation data as input for the models was demonstrated.",
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"title": "BallMerge: High‐quality Fast Surface Reconstruction via Voronoi Balls",
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"abstract": "We introduce a Delaunay-based algorithm for reconstructing the underlying surface of a given set of unstructured points in 3D. The implementation is very simple, and it is designed to work in a parameter-free manner. The solution builds upon the fact that in the continuous case, a closed surface separates the set of maximal empty balls (medial balls) into an interior and exterior. Based on discrete input samples, our reconstructed surface consists of the interface between Voronoi balls, which approximate the interior and exterior medial balls. An initial set of Voronoi balls is iteratively processed, merging Voronoi-ball pairs if they fulfil an overlapping error criterion. Our complete open-source reconstruction pipeline performs up to two quick linear-time passes on the Delaunay complex to output the surface, making it an order of magnitude faster than the state of the art while being competitive in memory usage and often superior in quality. We propose two variants (local and global), which are carefully designed to target two different reconstruction scenarios for watertight surfaces from accurate or noisy samples, as well as real-world scanned data sets, exhibiting noise, outliers, and large areas of missing data. The results of the global variant are, by definition, watertight, suitable for numerical analysis and various applications (e.g., 3D printing). Compared to classical Delaunay-based reconstruction techniques, our method is highly stable and robust to noise and outliers, evidenced via various experiments, including on real-world data with challenges such as scan shadows, outliers, and noise, even without additional preprocessing.",
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"title": "Spatial Organization Strategies in Exploratory Analysis of Unstructured Data",
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"abstract": "As not only the amount but also the complexity of data increases, there is a growing need to support humans in the analysis of data that is not structured in a way that can be easily interpreted by machines. So-called “knowledge-assisted visual analytics” (KAVA) tools aim to address these challenges by integrating the knowledge of the analyst into their system to support the analysis process.In this thesis, we investigate the spatial organization strategies that users employ when exploring unstructured data. We aim to characterize the types of strategies that users employ, how they change over time, and how we can use them to infer the users’ knowledge of the data. To answer these questions, we first conduct a user study in which the participants explore an image dataset on a multitouch tabletop interface imitating an analogue setting and externalize their findings into concept maps. We observe their organization strategies and analyse their methods in a mixed-methods approach, combining qualitative analysis of the participants’ interview statements with quantitative analysis of the interaction logs.We find that the participants’ spatial organization strategies can be characterized by four features: semantic clusters, type of layout, uncovering process, and reorganization of the data. While most participants prefer layouts that give them an overview of the data, only about half create semantic clusters (i.e., grouping similar images together). The participants also mostly uncovered all images — which were initially on a stack — in the task right away before externalizing their knowledge, and only a few reorganized the images. We further find that the participants generally did not change their organization strategies over time, and that the resulting spatial arrangements do not necessarily provide valuable insights into the users’ knowledge of the data.Finally, we discuss our findings and list the limitations of our study. As this thesis is embedded in a research project that aims to develop a tool for knowledge-assisted visual analytics, we discuss potential design implications for the development of such a tool.",
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"title": "Visual Analytics für Deep Learning mit Graphen: Case Study Neuronen Clustering",
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"title": "HORA 3D: Personalized Flood Risk Visualization as an Interactive Web Service",
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