Joint Learning of Confidence Fusion, Semantic Alignment and Group-Guided Reliability: A Novel Semi-Supervised Learning Framework for 3D Medical Image Segmentation

Semi-supervised learning (SSL) has shown strong potential in reducing the reliance on large-scale voxel-level annotations for 3D medical image segmentation. However, existing SSL methods often suffer from unstable training and limited generalization due to unreliable pseudo-labels and insufficient structural modeling in unlabeled data. These challenges are especially evident in volumetric contexts, where anatomical structures exhibit high inter-class imbalance and complex spatial dependencies. To address these issues, we propose a semi-supervised framework built upon a single-network architecture that integrates feature learning, consistency regularization, and pseudo-label reliability modeling in a unified manner. The framework comprises three key components: 1) a Confidence-aware Multi-level Fusion Network (CMFN) for capturing robust multi-scale semantic representations; 2) a Semantic-Enhanced Center Alignment (SECA) module to align feature distributions of group-level anatomical structures and mitigate semantic drift in pseudo-labels; and 3) a Group-Guided Reliability Assessment (GGRA) module that enhances pseudo-label reliability by modeling confidence errors in a group-aware structural context. Together, these modules enhance both feature discriminability and the reliability of pseudo-labels.We evaluate our framework on three public 3D medical image segmentation benchmarks: LA, BTCV, and BraTS19. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches under limited annotation, achieving superior accuracy and generalization across diverse anatomical structures and segmentation tasks.