English

An evaluation of U-Net in Renal Structure Segmentation

Image and Video Processing 2022-09-07 v1 Computer Vision and Pattern Recognition Machine Learning
Authors: Haoyu Wang , Ziyan Huang , Jin Ye , Can Tu , Yuncheng Yang , Shiyi Du , Zhongying Deng , Chenglong Ma , Jingqi Niu , Junjun He
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Abstract

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.

Keywords

medical image segmentation

Cite

@article{arxiv.2209.02247,
  title  = {An evaluation of U-Net in Renal Structure Segmentation},
  author = {Haoyu Wang and Ziyan Huang and Jin Ye and Can Tu and Yuncheng Yang and Shiyi Du and Zhongying Deng and Chenglong Ma and Jingqi Niu and Junjun He},
  journal= {arXiv preprint arXiv:2209.02247},
  year   = {2022}
}

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