# A Multi-Stage Framework for the 2022 Multi-Structure Segmentation for Renal Cancer Treatment

arXiv: https://arxiv.org/abs/2207.09165 · HTML: https://mlsanity.com/paper/2207.09165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 19 Jul 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)
- [Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT](https://mlsanity.com/paper/2109.05816/) (2021)
- [Organ Shape Sensing using Pneumatically Attachable Flexible Rails in Robotic-Assisted Laparoscopic Surgery](https://mlsanity.com/paper/2202.10977/) (2022)

## Abstract (arXiv)

Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is of great clinical significance. Automatic segmentation of kidney, renal tumor, renal vein and renal artery benefits a lot on surgery-based renal cancer treatment. In this paper, we propose a new nnhra-unet network, and use a multi-stage framework which is based on it to segment the multi-structure of kidney and participate in the KiPA2022 challenge.