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

arXiv 2207.09165 ↗First posted 19 Jul 2022eess.IV cs.CVcited by 0

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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.

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.

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References · 3

  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  2. 2021Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CTcited by 2
  3. 2022Organ Shape Sensing using Pneumatically Attachable Flexible Rails in Robotic-Assisted Laparoscopic Surgerycited by 1

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