Abstract
Automated segmentation of aneurysms from 3D CT is important for the diagnosis, monitoring, and treatment planning of the cerebral aneurysm disease. This short paper briefly presents the main technique details of the aneurysm segmentation method in MICCAI 2020 CADA challenge. The main contribution is that we configure the 3D U-Net with a large patch size, which can obtain the large context. Our method ranked second on the MICCAI 2020 CADA testing dataset with an average Jaccard of 0.7593. Our code and trained models are publicly available at https://github.com/JunMa11/CADA2020.
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References
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 11531005, No. 11971229). We are grateful to the High Performance Computing Center of Nanjing University for supporting the blade cluster system to run the experiments. We also highly appreciate the CADA organizers for holding the great challenge and creating the publicly available dataset.
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Ma, J., Nie, Z. (2021). Exploring Large Context for Cerebral Aneurysm Segmentation. 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_7
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DOI: https://doi.org/10.1007/978-3-030-72862-5_7
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