论文标题
使用CTA图像的肾脏多结构分割的大环境
Using Large Context for Kidney Multi-Structure Segmentation from CTA Images
论文作者
论文摘要
来自3D CTA的多结构（即肾脏，肾脏，动脉和静脉）的准确和自动分割是基于手术的肾脏癌治疗的最重要任务之一（例如，腹腔镜部分肾切除术）。本文简要介绍了MICCAI 2022 KIPA挑战中多结构SEG-Interation方法的主要技术细节。本文的主要贡献是我们设计了具有较大上下文信息封盖功能的3D UNET。我们的方法在MICCAI 2022 KIPA CHAL-LENGE开放测试数据集上排名第八，平均位置为8.2。我们的代码和训练有素的模型可在https://github.com/fengjiejiejiejie/kipa22_nnunet上公开获得。
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.
