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Now showing 1 - 10 of 37
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    A Survey of Visual Analytics for Public Health
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Preim, Bernhard; Lawonn, Kai; Benes, Bedrich and Hauser, Helwig
    We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.
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    Implicit Modeling of Patient-Specific Aortic Dissections with Elliptic Fourier Descriptors
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Mistelbauer, Gabriel; Rössl, Christian; Bäumler, Kathrin; Preim, Bernhard; Fleischmann, Dominik; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Aortic dissection is a life-threatening vascular disease characterized by abrupt formation of a new flow channel (false lumen) within the aortic wall. Survivors of the acute phase remain at high risk for late complications, such as aneurysm formation, rupture, and death. Morphologic features of aortic dissection determine not only treatment strategies in the acute phase (surgical vs. endovascular vs. medical), but also modulate the hemodynamics in the false lumen, ultimately responsible for late complications. Accurate description of the true and false lumen, any communications across the dissection membrane separating the two lumina, and blood supply from each lumen to aortic branch vessels is critical for risk prediction. Patient-specific surface representations are also a prerequisite for hemodynamic simulations, but currently require time-consuming manual segmentation of CT data. We present an aortic dissection cross-sectional model that captures the varying aortic anatomy, allowing for reliable measurements and creation of high-quality surface representations. In contrast to the traditional spline-based cross-sectional model, we employ elliptic Fourier descriptors, which allows users to control the accuracy of the cross-sectional contour of a flow channel. We demonstrate (i) how our approach can solve the requirements for generating surface and wall representations of the flow channels, (ii) how any number of communications between flow channels can be specified in a consistent manner, and (iii) how well branches connected to the respective flow channels are handled. Finally, we discuss how our approach is a step forward to an automated generation of surface models for aortic dissections from raw 3D imaging segmentation masks.
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    A Geometric Optimization Approach for the Detection and Segmentation of Multiple Aneurysms
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Lawonn, Kai; Meuschke, Monique; Wickenhöfer, Ralph; Preim, Bernhard; Hildebrandt, Klaus; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We present a method for detecting and segmenting aneurysms in blood vessels that facilitates the assessment of risks associated with the aneurysms. The detection and analysis of aneurysms is important for medical diagnosis as aneurysms bear the risk of rupture with fatal consequences for the patient. For risk assessment and treatment planning, morphological descriptors, such as the height and width of the aneurysm, are used. Our system enables the fast detection, segmentation and analysis of single and multiple aneurysms. The method proceeds in two stages plus an optional third stage in which the user interacts with the system. First, a set of aneurysm candidate regions is created by segmenting regions of the vessels. Second, the aneurysms are detected by a classification of the candidates. The third stage allows users to adjust and correct the result of the previous stages using a brushing interface. When the segmentation of the aneurysm is complete, the corresponding ostium curves and morphological descriptors are computed and a report including the results of the analysis and renderings of the aneurysms is generated. The novelty of our approach lies in combining an analytic characterization of aneurysms and vessels to generate a list of candidate regions with a classifier trained on data to identify the aneurysms in the candidate list. The candidate generation is modeled as a global combinatorial optimization problem that is based on a local geometric characterization of aneurysms and vessels and can be efficiently solved using a graph cut algorithm. For the aneurysm classification scheme, we identified four suitable features and modeled appropriate training data. An important aspect of our approach is that the resulting system is fast enough to allow for user interaction with the global optimization by specifying additional constraints via a brushing interface.
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    Visual Analytics to Support Treatment Decisions in Late-Stage Melanoma Patients
    (The Eurographics Association, 2023) Pereira, Calida; Niemann, Uli; Braun, Andreas; Mengoni, Miriam; Tüting, Thomas; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    We present a visual analytics system to support treatment decisions in late-stage Melanoma patients. With the aim of improving patient outcomes, personalized treatment decisions based on individual characteristics and medical histories are crucial. The research focuses on the design and development of a visual analytics system tailored specifically for tumor boards, where multidisciplinary teams collaborate to make informed decisions. By leveraging a comprehensive database containing treatment and tumor stage progression information from over 1100 patients, the system provides healthcare professionals with a holistic overview and facilitates the analysis of individual cases as well as comparisons between multiple patients. The distinction between tumor board preparation systems and systems used during discussions is emphasized to ensure user-centric design and usability. Through the use of visual analytics techniques, complex relationships between treatment outcomes, temporal features, and patient-specific factors are explored, enabling clinicians to identify patterns and trends that may impact treatment decisions. The findings of this research contribute to the growing field of visual analytics in healthcare and have the potential to enhance treatment decision-making and patient care in late-stage cancer scenarios.
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    A Visual Analytics Approach for Patient Stratification and Biomarker Discovery
    (The Eurographics Association, 2019) Alemzadeh, Shiva; Kromp, Florian; Preim, Bernhard; Taschner-Mandl, Sabine; Bühler, Katja; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    We introduce discoVA as a visual analytics tool for the refinement of risk stratification of cancer patients and biomarker discovery. Currently, tools for the joint analysis of multiple biological and clinical information in this field are insufficient or lacking. Our tool fills this gap by enabling bio-medical experts to explore datasets of cancer patient cohorts. By using multiple coordinated visualization techniques, nested visual queries on various data types can be performed to generate/prove a hypothesis by identifying discrete sub-cohorts. We demonstrated the utility of discoVA by a case study involving bio-medical researchers.
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    COMFIS - Comparative Visualization of Simulated Medical Flow Data
    (The Eurographics Association, 2022) Meuschke, Monique; Voß, Samuel; Eulzer, Pepe; Janiga, Gabor; Arens, Christoph; Wickenhöfer, Ralph; Preim, Bernhard; Lawonn, Kai; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun Wu
    Simulations of human blood and airflow are playing an increasing role in personalized medicine. Comparing flow data of different treatment scenarios or before and after an intervention is important to assess treatment options and success. However, existing visualization tools are either designed for the evaluation of a single data set or limit the comparison to a few partial aspects such as scalar fields defined on the vessel wall or internal flow patterns. Therefore, we present COMFIS, a system for the comparative visual analysis of two simulated medical flow data sets, e.g. before and after an intervention. We combine various visualization and interaction methods for comparing different aspects of the underlying, often time-dependent data. These include comparative views of different scalar fields defined on the vessel/mucous wall, comparative depictions of the underlying volume data, and comparisons of flow patterns. We evaluated COMFIS with CFD engineers and medical experts, who were able to efficiently find interesting data insights that help to assess treatment options.
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    Aneulysis - A System for Aneurysm Data Analysis
    (The Eurographics Association, 2020) Meuschke, Monique; Wickenhöfer, Ralph; Preim, Bernhard; Lawonn, Kai; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
    We present ANEULYSIS, a system to improve risk assessment and treatment planning of cerebral aneurysms. Aneurysm treatment must be carefully examined as there is a risk of fatal outcome during surgery. Aneurysm growth, rupture, and treatment success depend on the interplay of vascular morphology and hemodynamics. Blood flow simulations can obtain the patient-specific hemodynamics. However, analyzing the time-dependent, multi-attribute data is time-consuming and error-prone. ANEULYSIS supports the analysis and visual exploration of aneurysm data including morphological and hemodynamic attributes. Since this is an interdisciplinary process involving both physicians and fluid mechanics experts, we provide a redundancy-free management of aneurysm data sets according to a consistent structure. Major contributions are an improved analysis of morphological aspects, simultaneous evaluation of wall- and flow-related characteristics as well as multiple attributes on the vessel wall, the assessment of mechanical wall processes as well as an automatic classification of the internal flow behavior. It was designed and evaluated in collaboration with domain experts who confirmed its usefulness and clinical necessity.
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    Reflections on AI-Assisted Character Design for Data-Driven Medical Stories
    (The Eurographics Association, 2023) Budich, Beatrice; Garrison, Laura A.; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    Data-driven storytelling has experienced significant growth in recent years to become a common practice in various application areas, including healthcare. Within the realm of medical narratives, characters play a pivotal role in connecting audiences with data and conveying complex medical information in an engaging manner that may influence positive behavioral and lifestyle changes on the part of the viewer. However, the process of designing characters that are both informative and engaging remains a challenge. In this paper, we propose an AI-assisted pipeline for character design in the context of data-driven medical stories. Our iterative pipeline blends design sensibilities with automation to reduce the time and artistic expertise needed to develop characters reflective of the underlying data, even when that data is time-oriented as in a cohort study.
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    An Exploration of Practice and Preferences for the Visual Communication of Biomedical Processes
    (The Eurographics Association, 2021) Garrison, Laura; Meuschke, Monique; Fairman, Jennifer; Smit, Noeska N.; Preim, Bernhard; Bruckner, Stefan; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
    The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design.
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    Transdisciplinary Visualization of Aortic Dissections
    (The Eurographics Association, 2023) Mistelbauer, Gabriel; Bäumler, Kathrin; Mastrodicasa, Domenico; Hahn, Lewis D.; Pepe, Antonio; Sandfort, Veit; Hinostroza, Virginia; Ostendorf, Kai; Schroeder, Aaron; Sailer, Anna M.; Willemink, Martin J.; Walters, Shannon; Preim, Bernhard; Fleischmann, Dominik; Raidou, Renata; Kuhlen, Torsten
    Aortic dissection is a life-threatening condition caused by the abrupt formation of a secondary blood flow channel within the vessel wall. Patients surviving the acute phase remain at high risk for late complications, such as aneurysm formation and aortic rupture. The timing of these complications is variable, making long-term imaging surveillance crucial for aortic growth monitoring. Morphological characteristics of the aorta, its hemodynamics, and, ultimately, risk models impact treatment strategies. Providing such a wealth of information demands expertise across a broad spectrum to understand the complex interplay of these influencing factors. We present results of our longstanding transdisciplinary efforts to confront this challenge. Our team has identified four key disciplines, each requiring specific expertise overseen by radiology: lumen segmentation and landmark detection, risk predictors and inter-observer analysis, computational fluid dynamics simulations, and visualization and modeling. In each of these disciplines, visualization supports analysis and serves as communication medium between stakeholders, including patients. For each discipline, we summarize the work performed, the related work, and the results.