CHCL-Seg: A Competitive Hierarchical Contrastive Learning Framework for Semi-Supervised 3D Medical Image Segmentation

3D medical image segmentation plays an important role in intelligent clinical applications. Although existing deep learning methods have achieved substantial progress in this field, they are limited by the high cost of 3D manual annotation. Semi-supervised learning, which combines a small amount of labeled data with a large amount of unlabeled data for training, is an effective approach to alleviate label scarcity. However, existing semi-supervised methods for 3D medical image segmentation still face the following problems: confirmation bias is difficult to effectively mitigate, target boundary segmentation accuracy is insufficient, and cross-dataset semantic representation is difficult. To address these challenges, this paper proposes a competitive hierarchical contrastive learning framework for semi-supervised 3D medical image segmentation, termed CHCL-Seg. We first construct a dual-branch network with shared parameters, and introduce an uncertainty-aware competitive loss (UnCL) to balance noise filtering and boundary information utilization, thereby mitigating confirmation bias. We then propose a hierarchical contrastive learning mechanism, where at the high level, the Dense-OT module performs class prototype alignment to alleviate class imbalance, and at the low level, the V-DACL module mines hard boundary samples to enhance boundary feature discrimination. Extensive experiments on three public datasets, LA, BraTS-2019, and ACDC demonstrate that the proposed method comprehensively outperforms existing state-of-the-art semi-supervised segmentation methods. Our code will be released upon acceptance.