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Shit, S.* ; Ezhov, I.* ; Paetzold, J.C. ; Menze, B.*

A ν -Net: Automatic detection and segmentation of aneurysm.

In: International workshop on Cerebral Aneurysm Detection. Berlin [u.a.]: Springer, 2021. 51-57 (Lect. Notes Comput. Sc. ; 12643 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
We propose an automatic solution for the CADA 2020 challenge to detect aneurysm from Digital Subtraction Angiography (DSA) images. Our method relies on 3D U-net as the backbone and heavy data augmentation with a carefully chosen loss function. We were able to generalize well using our solution (despite training on a small dataset) that is demonstrated through accurate detection and segmentation on the test data.
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Publikationstyp Artikel: Sammelbandbeitrag/Buchkapitel
Korrespondenzautor
Schlagwörter Aneurysm ; Detection ; Segmentation
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Bandtitel International workshop on Cerebral Aneurysm Detection
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 12643 LNCS, Heft: , Seiten: 51-57 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)