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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Acknowledgement
Suprosanna Shit and Ivan Ezhov are supported by the Translational Brain Imaging Training Network (TRABIT) under the European Union’s ‘Horizon 2020’ research & innovation program (Grant agreement ID: 765148). Johannes C. Paetzold and Suprosanna Shit are supported by the Graduate School of Bioengineering, Technical University of Munich.
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Shit, S., Ezhov, I., Paetzold, J.C., Menze, B. (2021). A\(\nu \)-Net: Automatic Detection and Segmentation of Aneurysm. In: Hennemuth, A., Goubergrits, L., Ivantsits, M., Kuhnigk, JM. (eds) Cerebral Aneurysm Detection and Analysis. CADA 2020. Lecture Notes in Computer Science(), vol 12643. Springer, Cham. https://doi.org/10.1007/978-3-030-72862-5_5
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DOI: https://doi.org/10.1007/978-3-030-72862-5_5
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