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

arXiv 2208.04525 ↗First posted 9 Aug 2022eess.IV cs.CVcited by 0

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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.

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References · 3

  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  2. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 328
  3. 2020Exploring Large Context for Cerebral Aneurysm Segmentationcited by 2

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