Using Large Context for Kidney Multi-Structure Segmentation from CTA Images
This addresses a domain-specific problem for medical imaging in renal cancer treatment, but it is incremental as it builds on existing methods like 3D UNet.
The paper tackled automated segmentation of kidneys, tumors, arteries, and veins from 3D CTA images for renal cancer surgery, achieving eighth place in the MICCAI 2022 KIPA challenge with a mean position of 8.2.
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.