Using Large Context for Kidney Multi-Structure Segmentation from CTA Images

TL;DR

This paper introduces a 3D UNet model that leverages large context information for improved multi-structure segmentation of kidneys and related tissues from CTA images, aiding renal cancer surgery planning.

Contribution

We designed a 3D UNet with enhanced large context capturing ability for multi-structure segmentation in CTA images, demonstrating competitive performance in MICCAI 2022 challenge.

Findings

Ranked eighth in MICCAI 2022 KIPA challenge

Achieved a mean position of 8.2 on the test dataset

Model and code are publicly available

Abstract

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.