VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data

Simple item page
dc.contributor.authorThamm, Florianen_US
dc.contributor.authorJürgens, Markusen_US
dc.contributor.authorDitt, Hendriken_US
dc.contributor.authorMaier, Andreasen_US
dc.contributor.editorKozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgiaen_US
dc.date.accessioned2020-09-28T06:12:08Z
dc.date.available2020-09-28T06:12:08Z
dc.date.issued2020
dc.description.abstractComputed Tomography Angiography (CTA) is one of the most commonly used modalities in the diagnosis of cerebrovascular diseases like ischemic strokes. Usually, the anatomy of interest in ischemic stroke cases is the Circle of Willis and its peripherals, the cerebral arteries, as these vessels are the most prominent candidates for occlusions. The diagnosis of occlusions in these vessels remains challenging, not only because of the large amount of surrounding vessels but also due to the large number of anatomical variants. We propose a fully automated image processing and visualization pipeline, which provides a full segmentation and modelling of the cerebral arterial tree for CTA data. The model itself enables the interactive masking of unimportant vessel structures e.g. veins like the Sinus Sagittalis, and the interactive planning of shortest paths meant to be used to prepare further treatments like a mechanical thrombectomy. Additionally, the algorithm automatically labels the cerebral arteries (Middle Cerebral Artery left and right, Anterior Cerebral Artery short, Posterior Cerebral Artery left and right) detects occlusions or interruptions in these vessels. The proposed pipeline does not require a prior non-contrast CT scan and achieves a comparable segmentation appearance as in a Digital Subtraction Angiography (DSA).en_US
dc.description.sectionheadersVascular and Flow
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20201181
dc.identifier.isbn978-3-03868-109-0
dc.identifier.issn2070-5786
dc.identifier.pages151-155
dc.identifier.urihttps://doi.org/10.2312/vcbm.20201181
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20201181
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectImage segmentation
dc.subjectSearch with partial observations
dc.subjectHuman centered computing
dc.subjectVisualization
dc.titleVirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Dataen_US
Files
Original bundle
Now showing 1 - 4 of 4
Thumbnail Image
Name:
151-155.pdf
Size:
582.56 KB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
virtualdsa_1.mp4
Size:
36.48 MB
Format:
Unknown data format
Download
No Thumbnail Available
Name:
virtualdsa_2.mp4
Size:
16.02 MB
Format:
Unknown data format
Download
No Thumbnail Available
Name:
virtualdsa_3.mp4
Size:
29.38 MB
Format:
Unknown data format
Download
Collections
VCBM 2020: Eurographics Workshop on Visual Computing for Biology and Medicine