{
  "id": "2301.05500",
  "version": "v1",
  "published": "2023-01-13T12:03:58.000Z",
  "updated": "2023-01-13T12:03:58.000Z",
  "title": "RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation",
  "authors": [
    "Xiangyu Zhao",
    "Zengxin Qi",
    "Sheng Wang",
    "Qian Wang",
    "Xuehai Wu",
    "Ying Mao",
    "Lichi Zhang"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss to the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-01-13T12:03:58.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "semi-supervised medical image segmentation",
      "rectified contrastive pseudo supervision",
      "yields better segmentation performance",
      "segmentation method",
      "semi-supervised segmentation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}