Accurate and automated segmentation of multi-structure (i.e., kidneys, renal tu-mors, arteries, and veins) from 3D CTA is one of the most important tasks for surgery-based renal cancer treatment (e.g., laparoscopic partial nephrectomy). This paper briefly presents the main technique details of the multi-structure seg-mentation method in MICCAI 2022 KIPA challenge. The main contribution of this paper is that we design the 3D UNet with the large context information cap-turing capability. Our method ranked eighth on the MICCAI 2022 KIPA chal-lenge open testing dataset with a mean position of 8.2. Our code and trained models are publicly available at https://github.com/fengjiejiejiejie/kipa22_nnunet.

翻译：从三维CTA图像中准确自动分割多结构（即肾脏、肾肿瘤、动脉和静脉）是基于手术的肾癌治疗方案（例如腹腔镜部分肾切除术）中最重要的任务之一。本文简要介绍了MICCAI 2022 KIPA挑战赛中多结构分割方法的主要技术细节。本文的主要贡献在于设计了具备大上下文信息捕获能力的3D UNet。我们的方法在MICCAI 2022 KIPA挑战赛开放测试数据集中以8.2的平均分位列第八。我们的代码和训练模型已在https://github.com/fengjiejiejiejie/kipa22_nnunet 公开提供。