Abstract
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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CITATION STYLE
Shit, S., Ezhov, I., Paetzold, J. C., & Menze, B. (2021). A ν -Net: Automatic Detection and Segmentation of Aneurysm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12643 LNCS, pp. 51–57). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72862-5_5
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