Eurovis: Eurographics Conference on Visualization

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https://diglib.eg.org/handle/10.2312/392

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Browsing Eurovis: Eurographics Conference on Visualization by Subject "Applied computing"

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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.
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    Visual Assistance in Clinical Decision Support
    (The Eurographics Association, 2021) Müller, Juliane; Cypko, Mario; Oeser, Alexander; Stoehr, Matthäus; Zebralla, Veit; Schreiber, Stefanie; Wiegand, Susanne; Dietz, Andreas; Oeltze-Jafra, Steffen; Oeltze-Jafra, Steffen and Raidou, Renata Georgia
    Clinical decision-making for complex diseases such as cancer aims at finding the right diagnosis, optimal treatment or best aftercare for a specific patient. The decision-making process is very challenging due to the distributed storage of patient information entities in multiple hospital information systems, the required inclusion of multiple clinical disciplines with their different views of disease and therapy, and the multitude of available medical examinations, therapy options and aftercare strategies. Clinical Decision Support Systems (CDSS) address these difficulties by presenting all relevant information entities in a concise manner and providing a recommendation based on interdisciplinary disease- and patient-specific models of diagnosis and treatment. This work summarizes our research on visual assistance for therapy decision-making. We aim at supporting the preparation and implementation of expert meetings discussing cancer cases (tumor boards) and the aftercare consultation. In very recent work, we started to address the generation of models underlying a CDSS. The developed solutions combine state-of-the-art interactive visualizations with methods from statistics, machine learning and information organization.
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    Visualizing Carotid Stenoses for Stroke Treatment and Prevention
    (The Eurographics Association, 2023) Eulzer, Pepe; Richter, Kevin; Hundertmark, Anna; Meuschke, Monique; Wickenhöfer, Ralph; Klingner, Carsten M.; Lawonn, Kai; Raidou, Renata; Kuhlen, Torsten
    Analyzing carotid stenoses - potentially lethal constrictions of the brain-supplying arteries - is a critical task in clinical stroke treatment and prevention. Determining the ideal type of treatment and point for surgical intervention to minimize stroke risk is considerably challenging. We propose a collection of visual exploration tools to advance the assessment of carotid stenoses in clinical applications and research on stenosis formation. We developed methods to analyze the internal blood flow, anatomical context, vessel wall composition, and to automatically and reliably classify stenosis candidates. We do not presume already segmented and extracted surface meshes but integrate streamlined model extraction and pre-processing along with the result visualizations into a single framework. We connect multiple sophisticated processing stages in one user interface, including a neural prediction network for vessel segmentation and automatic global diameter computation. We enable retrospective user control over each processing stage, greatly simplifying error detection and correction. The framework was developed and evaluated in multiple iterative user studies, involving a group of eight specialists working in stroke care (radiologists and neurologists). It is publicly available, along with a database of over 100 carotid bifurcation geometries that were extracted with the framework from computed tomography data. Further, it is a vital part of multiple ongoing studies investigating stenosis pathophysiology, stroke risk, and the necessity for surgical intervention.