@article{raidou_2020Onc, title = "Principles of Visualization in Radiation Oncology", author = "Matthias Schlachter and Bernhard Preim and Katja B\"{u}hler and Renata Raidou", year = "2020", 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.", month = jan, doi = "https://doi.org/10.1159/000504940", journal = "Oncology and Informatics", volume = "1", pages = "1--11", URL = "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_2020Onc/", } @article{amirkhanov-2019-manylands, title = "ManyLands: A Journey Across 4D Phase Space of Trajectories", author = "Aleksandr Amirkhanov and Ilona Kosiuk and Peter Szmolyan and Artem Amirkhanov and Gabriel Mistelbauer and Eduard Gr\"{o}ller and Renata Raidou", year = "2019", 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.", month = oct, journal = "Computer Graphics Forum", volume = "38", number = "7", doi = "10.1111/cgf.13828", pages = "191--202", keywords = "Visual analytics, Web-based interaction", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/amirkhanov-2019-manylands/", } @misc{grossmann_2019_pelvisrunner_poster, title = "Pelvis Runner: A Visual Analytics Tool for Pelvic Organ Variability Exploration in Prostate Cancer Cohorts", author = "Nicolas Grossmann and Oscar Casares-Magaz and Ludvig Paul Muren and Vitali Moiseenko and John P. Einck and Eduard Gr\"{o}ller and Renata Raidou", year = "2019", 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.", month = oct, event = "IEEE VIS VAST", Conference date = "Poster presented at IEEE VIS VAST (2019-10-20--2019-10-25)", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/grossmann_2019_pelvisrunner_poster/", } @inproceedings{raidou_2019_pelvisrunner, title = "Pelvis Runner: Visualizing Pelvic Organ Variability in a Cohort of Radiotherapy Patients", author = "Nicolas Grossmann and Oscar Casares-Magaz and Ludvig Paul Muren and Vitali Moiseenko and John P. Einck and Eduard Gr\"{o}ller and Renata Raidou", year = "2019", 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.", month = sep, event = "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", doi = "10.2312/vcbm.20191233", booktitle = "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", pages = "69--78", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_pelvisrunner/", } @inproceedings{raidou_2019_preha, title = "preha: Establishing Precision Rehabilitation with Visual Analytics", author = "Georg Bernold and Kresimir Matkovic and Eduard Gr\"{o}ller and Renata Raidou", year = "2019", 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.", month = sep, event = "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", doi = "10.2312/vcbm.20191234", booktitle = "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", pages = "79--89", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_preha/", } @article{vitruvian_2019, title = "The Vitruvian Baby: Interactive Reformation of Fetal Ultrasound Data to a T-Position", author = "Eric M\"{o}rth and Renata Raidou and Ivan Viola and Noeska Natasja Smit", year = "2019", abstract = "Three-dimensional (3D) ultrasound imaging and visualization is often used in medical diagnostics, especially in prenatal screening. 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 in a 2D slice view, where precise measurements can be difficult to acquire due to the fetal pose. Performing the analysis in a 3D view would enable the viewer to better discriminate between artefacts and representative information. Additionally making data comparable between different investigations and patients is a goal in medical imaging techniques and is often achieved by standardization. With this paper, we introduce a novel approach to provide a standardization method for 3D ultrasound fetus screenings. Our approach is called “The Vitruvian Baby” and incorporates a complete pipeline for standardized measuring in 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 transformation of the 3D ultrasound data, we create an abstract representation of the fetus based on accurate measurements. We demonstrate the accuracy of our approach on simulated data where the ground truth is known. ", month = sep, journal = "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", volume = "9", doi = "10.2312/vcbm.20191245", pages = "201--205", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/vitruvian_2019/", } @incollection{raidou_2019_springer, title = "Visual Analytics for the Representation, Exploration and Analysis of High-Dimensional, Multi-Faceted Medical Data", author = "Renata Raidou", year = "2019", abstract = "Medicine is among research fields with a significant impact on humans and their health. Already for decades, medicine has established a tight coupling with the visualization domain, proving the importance of developing visualization techniques, designed exclusively for this research discipline. However, medical data is steadily increasing in complexity with the appearance of heterogeneous, multi-modal, multiparametric, cohort or population, as well as uncertain data. To deal with this kind of complex data, the field of Visual Analytics has emerged. In this chapter, we discuss the many dimensions and facets of medical data. Based on this classification, we provide a general overview of state-of-the-art visualization systems and solutions dealing with highdimensional, multi-faceted data. Our particular focus will be on multimodal, multi-parametric data, on data from cohort or population studies and on uncertain data, especially with respect to Visual Analytics applications for the representation, exploration, and analysis of highdimensional, multi-faceted medical data.", month = jul, 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", publisher = "Springer", volume = "2", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_springer/", } @article{raidou_star2019, title = "State-of-the-Art Report: Visual Computing in Radiation Therapy Planning", author = "Matthias Schlachter and Renata Raidou and Ludvig Paul Muren and Bernhard Preim and Katja B\"{u}hler", year = "2019", abstract = "Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which requires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of RT planning, providing solutions in all parts of the process—from imaging to delivery. Despite the significant technological advancements of RT over the last decades, there are still many challenges to address. This survey provides an overview of the compound planning process of RT, and of the ways that VC has supported RT in all its facets. The RT planning process is described to enable a basic understanding in the involved data, users and workflow steps. A systematic categorization and an extensive analysis of existing literature in the joint VC/RT research is presented, covering the entire planning process. The survey concludes with a discussion on lessons learnt, current status, open challenges, and future directions in VC/RT research.", month = jun, journal = "Computer Graphics Forum", volume = "3", number = "38", pages = "753--779", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_star2019/", } @inproceedings{raidou_RO2019, title = "PO-0962 Bladder changes during first week of RT for prostate cancer determine the risk of urinary toxicity", author = "Oscar Casares-Magaz and Renata Raidou and NJ Pettersson and Vitali Moiseenko and John P. Einck and A Hopper and R Knopp and Ludvig Paul Muren", year = "2019", month = apr, event = "ESTRO 38", doi = "https://doi.org/10.1016/S0167-8140(19)31382-9", booktitle = "Radiotherapy and Oncology", pages = "S522--S523", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_RO2019/", } @article{raidou2019_prsps, title = "Relaxing Dense Scatter Plots with Pixel-Based Mappings", author = "Renata Raidou and Eduard Gr\"{o}ller and Martin Eisemann", year = "2019", 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.", month = mar, journal = "IEEE Transactions on Visualization and Computer Graphics", volume = "25", doi = "10.1109/TVCG.2019.2903956", pages = "1--12", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/raidou2019_prsps/", } @unknown{manylands_award, title = "EG VCBM 2019 Image Contest Award, people's choice—ManyLands: A Journey Across 4D Phase Space of Biological Systems", author = "Aleksandr Amirkhanov and Ilona Kosiuk and Peter Szmolyan and Artem Amirkhanov and Gabriel Mistelbauer and Eduard Gr\"{o}ller and Renata Raidou", year = "2019", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/manylands_award/", } @article{raidou2018visualflatter, title = "VisualFlatter - Visual Analysis of Distortions in the Projection of Biomedical Structures", author = "Nicolas Grossmann and Thomas K\"{o}ppel and Eduard Gr\"{o}ller and Renata Raidou", year = "2018", abstract = "Projections of complex anatomical or biological structures from 3D to 2D are often used by visualization and domain experts to facilitate inspection and understanding. Representing complex structures, such as organs or molecules, in a simpler 2D way often requires less interaction, while enabling comparability. However, the most commonly employed projection methods introduce size or shape distortions, in the resulting 2D representations. While simple projections display known distortion patterns, more complex projection algorithms are not easily predictable.We propose the VisualFlatter, a visual analysis tool that enables visualization and domain experts to explore and analyze projection-induced distortions, in a structured way. Our tool provides a way to identify projected regions with semantically relevant distortions and allows users to comparatively analyze distortion outcomes, either from alternative projection methods or due to different setups through the projection pipeline. The user is given the ability to improve the initial projection configuration, after comparing different setups. We demonstrate the functionality of our tool using four scenarios of 3D to 2D projections, conducted with the help of domain or visualization experts working on different application fields. We also performed a wider evaluation with 13 participants, familiar with projections, to assess the usability and functionality of the Visual Flatter.", month = sep, journal = "Eurographics Proceedings", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/", } @article{rraidou_clinical, title = "Uncertainty evaluation of image-based tumour control probability models in radiotherapy of prostate cancer using a visual analytic tool", author = "Oscar Casares-Magaz and Renata Raidou and Jarle Roervik and Anna Vilanova i Bartroli and Ludvig Paul Muren", year = "2018", 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.", month = jan, journal = "Physics and Imaging in Radiation Oncology", number = "5", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/rraidou_clinical/", } @article{EuroVis2018-ShortPapers-Reiter, title = "Comparative Visual Analysis of Pelvic Organ Segmentations", author = "Oliver Reiter and Marcel Breeuwer and Eduard Gr\"{o}ller and Renata Raidou", year = "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.", journal = "Computer Graphics Forum", doi = "10.2312/eurovisshort.20181075", pages = "037-041", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Reiter/", } @misc{raidou_bestphd, title = "EuroVis Best PhD Award 2018—Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline", author = "Renata Raidou", year = "2018", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_bestphd/", } @article{EuroVis2018-ShortPapers-Karall, title = "ChemoExplorer: A Dashboard for the Visual Analysis of ChemotherapyResponse in Breast Cancer Patients", author = "Nikolaus Karall and Eduard Gr\"{o}ller and Renata Raidou", year = "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.", journal = "Computer Graphics Forum", doi = "10.2312/eurovisshort.20181077", pages = "049-053", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/EuroVis2018-ShortPapers-Karall/", } @article{raidou_2018_bladderrunner, title = "Bladder Runner: Visual Analytics for the Exploration of RT-Induced Bladder Toxicity in a Cohort Study", author = "Renata Raidou and Oscar Casares-Magaz and Aleksandr Amirkhanov and Vitali Moiseenko and Ludvig Paul Muren and John P. Einck and Anna Vilanova i Bartroli and Eduard Gr\"{o}ller", year = "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.", journal = "Computer Graphics Forum", volume = "37", number = "3", issn = "1467-8659", doi = "10.1111/cgf.13413", pages = "205-216", pages = "205--216", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/raidou_2018_bladderrunner/", } @article{raidou2018_rv3, title = "Uncertainty Visualization: Recent Developments and Future Challenges inProstate Cancer Radiotherapy Planning", author = "Renata Raidou", year = "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.", journal = "EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3) 2018", booktitle = "EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3) 2018", pages = "013-017", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018_rv3/", } @article{rraidou_EG17, title = "Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline", author = "Renata Raidou and Marcel Breeuwer and Anna Vilanova i Bartroli", year = "2017", 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.", month = apr, journal = "Computer Graphics Forum (Proceedings of Eurographics)", volume = "36", booktitle = "Computer Graphics Forum (Proceedings of Eurographics)", URL = "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_EG17/", } @book{rraidou_phdbook, title = "Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline.", author = "Renata Raidou", year = "2017", month = mar, isbn = "ISBN987-90-386-4230-7", pages = "1-240", publisher = "TU Eindhoven", URL = "https://www.cg.tuwien.ac.at/research/publications/2017/rraidou_phdbook/", } @article{Groeller_2016_P4, title = "Visual Analytics for the Exploration and Assessment of Segmentation Errors", author = "Renata Raidou and Freek Marcelis and Marcel Breeuwer and Eduard Gr\"{o}ller and Anna Vilanova i Bartroli and Huub van de Wetering", year = "2016", 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 performance 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.", month = sep, journal = "Eurographics Workshop on Visual Computing for Biology and Medicine", pages = "193--202", URL = "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P4/", } @article{raidou_eurovis16, title = "Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response.", author = "Renata Raidou and Oscar Casares-Magaz and Ludvig Paul Muren and Uulke A van der Heide and Jarle Roervik and Marcel Breeuwer and Anna Vilanova i Bartroli", year = "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.", journal = "EuroVis - Eurographics/IEEE-VGTC Symposium on Visualization 2016", URL = "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/", } @article{malan_fluoro, title = "A fluoroscopy-based planning and guidance software tool for minimally invasive hip refixation by cement injection.", author = "DF Malan and SJ van der Walt and Renata Raidou and B van den Berg and BC Stoel and CP Botha and RG Nelissen and ER Valstar", year = "2016", abstract = "PURPOSE: In 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. METHODS: HipRFX 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. RESULTS: Based 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%). CONCLUSIONS: As 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.", journal = "International journal of computer assisted radiology and surgery,", number = "2", volume = "11", pages = "281--296", URL = "https://www.cg.tuwien.ac.at/research/publications/2016/malan_fluoro/", } @article{raidou_miccai16, title = "Employing Visual Analytics to Aid the Design of White Matter Hyperintensity Classifiers.", author = "Renata Raidou and Hugo J. Kuijf and Neda Sepasian and Nicola Pezzotti and Willem H. Bouvy and Marcel Breeuwer and Anna Vilanova i Bartroli", year = "2016", abstract = "Accurate segmentation of brain white matter hyperintensi-ties (WMHs) is important for prognosis and disease monitoring. To thisend, classi ers are often trained { usually, using T1 and FLAIR weightedMR images. Incorporating additional features, derived from di usionweighted MRI, could improve classi cation. However, the multitude ofdi usion-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 erent approach, introducing asemi-automated pipeline to select interactively features for WMH classi -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 usivity (MD), and RadialDi usivity (RD) { and secondarily,CSand Fractional Anisotropy (FA).The next step in the pipeline is to train a classi er with these features,and compare its results to a similar classi er, used in previous work withautomated feature selection. Finally, VA is employed again, to analyzeand understand the classi er performance and results.", journal = "Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)", URL = "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_miccai16/", } @article{raidou_vis15, title = "Orientation-Enhanced Parallel Coordinate Plots", author = "Renata Raidou and Martin Eisemann and Marcel Breeuwer and Elmar Eisemann and Anna Vilanova i Bartroli", year = "2015", journal = "IEEE transactions on visualization and computer graphics", volume = "22", number = "1", pages = "589--598", URL = "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_vis15/", } @article{raidou_EuroVis15, title = "Visual analytics for the exploration of tumor tissue characterization", author = "Renata Raidou and Uulke A van der Heide and Cuong V Dinh and Ghazaleh Ghobadi and Jesper Follsted Kallehauge and Marcel Breeuwer and Anna Vilanova i Bartroli", year = "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.", journal = "In Computer Graphics Forum", URL = "https://www.cg.tuwien.ac.at/research/publications/2015/raidou_EuroVis15/", } @article{raidou_vcbm14, title = "The iCoCooN:Integration of Cobweb Charts with Parallel Coordinates forVisual Analysis of DCE-MRI Modeling Variations", author = "Renata Raidou and Uulke A van der Heide and PJ van Houdt and Marcel Breeuwer and Anna Vilanova i Bartroli", year = "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", journal = "Eurographics Workshop on Visual Computing for Biology and Medicine ", URL = "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vcbm14/", } @article{raidou_vis14, title = "Visual analytics for the exploration of multiparametric cancer imaging", author = "Renata Raidou and Marta Paes Moreira and Wouter van Elmpt and Marcel Breeuwer and Anna Vilanova i Bartroli", year = "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.", journal = "In Visual Analytics Science and Technology (VAST), 2014 IEEE Conference on Visualization", URL = "https://www.cg.tuwien.ac.at/research/publications/2014/raidou_vis14/", }