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<p>Automatic segmentation of knee MR images plays an important role in the diagnosis and treatment of knee osteoarthritis. Existing deep learning-based methods usually require considerable annotated samples, and manual labeling of knee MR images is tedious and time-consuming. To address the above problem, we propose a novel semi-supervised method, named as Contour-Aware Contrastive Learning Network (CACL-Net), for segmentation of femoral cartilage, tibial internal cartilage, and tibial external cartilage in knee MR images. The CACL-Net takes an encoder-decoder structure similar to the V-Net as backbone, and adopts a novel contrastive learning auxiliary task called Progressive Encoding Module, which can maintain the key high-level semantic information of the image and attenuate the irrelevant low-level semantic information. We also coin a contour-based self-attention module to prompt the network to pay more attention to edge details during the decoding process, thereby obtaining accurate segmentation results. Extensive experimental results demonstrate the proposed CACL-Net outperforms some other semi-supervised methods for knee MR image segmentation, and shows the potential usage of CACL-Net in the domain of semi-supervised segmentation problems. Our code is available at <a href="https://github.com/ldcdm/CLASS-Net">https://github.com/ldcdm/CLASS-Net</a>.</p>

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Contour-Aware contrastive learning for 3D knee segmentation from MR images

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Xianda Dong,
  • Lei Zhang,
  • Xing Zhao,
  • Shoujun Zhou,
  • Yuanquan Wang,
  • Jun Xia,
  • Tao Zhang

摘要

Automatic segmentation of knee MR images plays an important role in the diagnosis and treatment of knee osteoarthritis. Existing deep learning-based methods usually require considerable annotated samples, and manual labeling of knee MR images is tedious and time-consuming. To address the above problem, we propose a novel semi-supervised method, named as Contour-Aware Contrastive Learning Network (CACL-Net), for segmentation of femoral cartilage, tibial internal cartilage, and tibial external cartilage in knee MR images. The CACL-Net takes an encoder-decoder structure similar to the V-Net as backbone, and adopts a novel contrastive learning auxiliary task called Progressive Encoding Module, which can maintain the key high-level semantic information of the image and attenuate the irrelevant low-level semantic information. We also coin a contour-based self-attention module to prompt the network to pay more attention to edge details during the decoding process, thereby obtaining accurate segmentation results. Extensive experimental results demonstrate the proposed CACL-Net outperforms some other semi-supervised methods for knee MR image segmentation, and shows the potential usage of CACL-Net in the domain of semi-supervised segmentation problems. Our code is available at https://github.com/ldcdm/CLASS-Net.