VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data
Thamm F, Jürgens M, Ditt H, Maier A (2020)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2020
Pages Range: 5
Conference Proceedings Title: Eurographics Workshop on Visual Computing for Biology and Medicine
Event location: Uni Tübingen
Abstract
Computed 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).
Authors with CRIS profile
Florian Thamm
Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Additional Organisation(s)
Involved external institutions
How to cite
APA:
Thamm, F., Jürgens, M., Ditt, H., & Maier, A. (2020). VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data. In K. Nieselt and R. G. Raidou (Eds.), Eurographics Workshop on Visual Computing for Biology and Medicine (pp. 5). Uni Tübingen, DE.
MLA:
Thamm, Florian, et al. "VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data." Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine, Uni Tübingen Ed. K. Nieselt and R. G. Raidou, 2020. 5.
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