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

arXiv: https://arxiv.org/abs/2208.04525 · HTML: https://mlsanity.com/paper/2208.04525/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [Exploring Large Context for Cerebral Aneurysm Segmentation](https://mlsanity.com/paper/2012.15136/) (2020)

## Abstract (arXiv)

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.