Výsledky vyhledávání - "Sobisch, Jannik"

Dissertation/ Thesis

Intracranial aneurysm rupture management: Comparing morphologic and deep learning features

Autor: Sobisch, Jannik

Intracranial Aneurysms are a prevalent vascular pathology present in 3-4% of the population with an inherent risk of rupture. The growing accessibility of angiography has led to a rising incidence of detected aneurysms. An accurate assessment of the

Externí odkaz: https://htwk-leipzig.qucosa.de/id/qucosa%3A87207
https://htwk-leipzig.qucosa.de/api/qucosa%3A87207/attachment/ATT-0/

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Periodical

Deep shape based intracranial aneurysm rupture prediction

Autor: Chen, Weijie, Astley, Susan M., Choi, June Ho, Bizjak, Žiga, Park, Wonhyoung, Sobisch, Jannik, Špiclin, Žiga

Publikováno v: Proceedings of SPIE; April 2024, Vol. 12927 Issue: 1 p129272I-129272I-8, 1163457p

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Automated intracranial vessel labeling with learning boosted by vessel connectivity, radii and spatial context

Autor: Sobisch, Jannik, Bizjak, Žiga, Chien, Aichi, Špiclin, Žiga

Publikováno v: Proc Mach Learn Res

Cerebrovascular diseases are among the world’s top causes of death and their screening and diagnosis rely on angiographic imaging. We focused on automated anatomical labeling of cerebral arteries that enables their cross-sectional quantification an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::35507ab3a810ca5486a5c3be081c300e
https://europepmc.org/articles/PMC10112880/

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Akademický článek

Automated intracranial vessel labeling with learning boosted by vessel connectivity, radii and spatial context.

Autor: Sobisch J; Laboratory of Imaging Technologies, Faculty of electrical engineering, University of Ljubljana.; Dept. of Radiological Sciences, David Geffen School of Medicine at UCLA., Bizjak Ž; Laboratory of Imaging Technologies, Faculty of electrical engineering, University of Ljubljana.; Dept. of Radiological Sciences, David Geffen School of Medicine at UCLA., Chien A; Laboratory of Imaging Technologies, Faculty of electrical engineering, University of Ljubljana.; Dept. of Radiological Sciences, David Geffen School of Medicine at UCLA., Špiclin Ž; Laboratory of Imaging Technologies, Faculty of electrical engineering, University of Ljubljana.; Dept. of Radiological Sciences, David Geffen School of Medicine at UCLA.

Publikováno v: Proceedings of machine learning research [Proc Mach Learn Res] 2022 Nov; Vol. 194, pp. 34-44.

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