KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation

Kangqing Ye, Peng Liu, Xiaoyang Zou, Qin Zhou, Guoyan Zheng
8/10/2022
eess.IVcs.CVcs.LG

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Abstract

Three-dimensional (3D) integrated renal structures (IRS) segmentation is important in clinical practice. With the advancement of deep learning techniques, many powerful frameworks focusing on medical image segmentation are proposed. In this challenge, we utilized the nnU-Net framework, which is the state-of-the-art method for medical image segmentation. To reduce the outlier prediction for the tumor label, we combine contour regularization (CR) loss of the tumor label with Dice loss and cross-entropy loss to improve this phenomenon.

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Cite this paper

@article{ye2022kipa,
  title  = {KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation},
  author = {Kangqing Ye and Peng Liu and Xiaoyang Zou and Qin Zhou and Guoyan Zheng},
  year   = {2022},
  eprint = {2208.05772},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2208.05772v2}
}

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