Semi-Supervised Medical Image Segmentation with Cross-View Consistency and Contrastive Learning

Medical image segmentation plays a crucial role in many clinical applications. To alleviate the dependency on massive annotations, semi-supervised learning has attracted increasing attention. However, these methods face significant intra-class and inter-class variation and do not fully utilize the critical multi-view information inherent in medical images. This study proposes a novel network, CV-Net, which integrates multi-view information for semi-supervised medical image segmentation. Concretely, the network is based on Mean-Teacher architecture which largely narrows the empirical distribution gap between labeled and unlabeled data. The proposed cross-view consistency regularization module incorporates a dual-branch attention architecture to integrate consistent semantics while focusing on details, enhancing feature extraction capabilities. The proposed bi-semantic contrastive learning module leverages limited labels and explore pseudo-labels to define semantically similar regions, enhancing the representation capacity. Experiments conducted on two datasets demonstrated the effectiveness of the proposed network. CV-Net showed significant improvements across four metrics, evident with both 5% and 10% labeled data. Specifically, with 5% labeled data, the mean Dice increased by 1.37%. Compared with previous state-of-the-art methods, CV-Net achieved the best results, notably reducing both intra-class and inter-class errors.