  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 the 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 \url{https://github.com/JunMa11/CADA2020}.
