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.

翻译：医学图像分割方法通常采用全监督设计以保证模型性能，这需要大量专家标注的样本，成本高昂且耗时。半监督图像分割通过利用大量未标注图像和少量标注图像缓解了这一问题。然而，从大量未标注图像中学习鲁棒表征仍具挑战性，因为伪标签中存在潜在噪声且特征空间中的类可分性不足，这削弱了当前半监督分割方法的性能。为解决上述问题，我们提出一种名为矫正对比伪监督（RCPS）的新型半监督分割方法，该方法结合了矫正伪监督与体素级对比学习，以提升半监督分割的有效性。具体而言，我们基于不确定性估计和一致性正则化为伪监督方法设计了一种新颖的矫正策略，以减少伪标签中的噪声影响。此外，我们在网络引入双向体素对比损失，以确保特征空间中的类内一致性和类间对比性，从而增强分割中的类可分性。所提出的RCPS分割方法已在两个公开数据集和一个内部临床数据集上得到验证。实验结果表明，与当前最先进的半监督医学图像分割方法相比，该方法取得了更优的分割性能。源代码已开源至https://github.com/hsiangyuzhao/RCPS。