论文标题
探索大脑动脉瘤分段的大环境
Exploring Large Context for Cerebral Aneurysm Segmentation
论文作者
论文摘要
从3D CT的动脉瘤自动分割对于诊断，监测和治疗计划的脑动脉瘤病很重要。这篇简短的论文简要介绍了MICCAI 2020 CADA挑战中动脉瘤分割方法的主要技术详细信息。主要贡献是我们配置具有较大贴片大小的3D U-NET，可以获得大环境。我们的方法在MICCAI 2020 CADA测试数据集中排名第二，平均jaccard为0.7593。我们的代码和训练有素的模型可在\ url {https://github.com/junma11/cada20202020}上公开获得。
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}.
