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dc.contributor.authorEulzer, Pepeen_US
dc.contributor.authorRichter, Kevinen_US
dc.contributor.authorHundertmark, Annaen_US
dc.contributor.authorMeuschke, Moniqueen_US
dc.contributor.authorWickenhöfer, Ralphen_US
dc.contributor.authorKlingner, Carsten M.en_US
dc.contributor.authorLawonn, Kaien_US
dc.contributor.editorRaidou, Renataen_US
dc.contributor.editorKuhlen, Torstenen_US
dc.date.accessioned2023-06-12T04:48:08Z
dc.date.available2023-06-12T04:48:08Z
dc.date.issued2023
dc.identifier.isbn978-3-03868-221-9
dc.identifier.urihttps://doi.org/10.2312/evm.20231086
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evm20231086
dc.description.abstractAnalyzing 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.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing -> Scientific visualization; Applied computing -> Life and medical sciences
dc.subjectHuman centered computing
dc.subjectScientific visualization
dc.subjectApplied computing
dc.subjectLife and medical sciences
dc.titleVisualizing Carotid Stenoses for Stroke Treatment and Preventionen_US
dc.description.seriesinformationEuroVis 2023 - Dirk Bartz Prize
dc.description.sectionheaders2nd Prize
dc.identifier.doi10.2312/evm.20231086
dc.identifier.pages7-11
dc.identifier.pages5 pages


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License