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An evaluation of U-Net in Renal Structure Segmentation

arXiv 2209.02247 ↗First posted 6 Sep 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications. Kidney PArsing~(KiPA 2022) Challenge aims to build a fine-grained multi-structure dataset and improve the segmentation of multiple renal structures. Recently, U-Net has dominated the medical image segmentation. In the KiPA challenge, we evaluated several U-Net variants and selected the best models for the final submission.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 5 references that are in the map. First posted on arXiv 6 Sep 2022.

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2 of 5 references.

References · 2

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentationcited by 291

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