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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 as soon as Postprint is submitted to ZB.
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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Publication type Article: Edited volume or book chapter
Corresponding Author
Keywords Aneurysm ; Detection ; Segmentation
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Book Volume Title International workshop on Cerebral Aneurysm Detection
Quellenangaben Volume: 12643 LNCS, Issue: , Pages: 51-57 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute for Tissue Engineering and Regenerative Medicine (ITERM)