A Stability-Aware Dual-Head Network with Prototype-Based Consistency for Semi-Supervised Medical Image Segmentation

Semi-supervised semantic segmentation for medical images has evolved through time. While it can leverage the unlabeled data to significantly improve the segmentation performance, it still suffers the problems of intra-class variance and the consequent class-domain distribution misalignment along with costly training. In this paper, a stability-aware dual-head architecture is proposed to synergize prototype-based and Fully Convolutional Network (FCN) methodologies. By integrating prototype-based method for feature consistency and FCN method for spatial detail preservation, our method enforces consistency between different feature representations. It combines the semantic consistency of prototype learning with the precision of dense prediction. A sample-level stability-aware adaptive augmentation strategy is introduced to further mitigate intra-class variance and distribution shifts. The following certainty guided fusion process dynamically refines the pseudo-labels, better utilizing the advantages in different methods. Experiments on BraTS2019 and LA Heart demonstrate State-Of-The-Art (SOTA) performance, achieving significant improvements over the previous SOTA methods on multiple metrics. The framework effectively bridges domain gaps and enhances pseudo-label reliability for medical image analysis. (Code is available at https://github.com/Alfredzly/SDNP).