Boundary-aware dynamic re-weighting for semi-supervised medial image segmentation

Different from traditional semi-supervised learning (SSL), semi-supervised medical image segmentation faces two significant challenges: (1) the imbalanced distribution of labeled data causes models to bias towards majority classes; (2) the distribution discrepancy between labeled and unlabeled samples induces confirmation bias in pseudo-labels. Inspired by clinical practice, where experienced doctors utilize intrinsic features from the interior of target organs to clarify ambiguous boundaries and focus on minority classes, we propose a novel boundary-aware dynamic re-weighting network (BDRN). First, we utilize edge filters to generate visually different but semantically aligned views, compelling two sub-networks to learn informative features from organ interiors and boundaries, respectively. Second, we extract the boundary and interior regions using morphological operators and introduce a shape constraint to enhance feature learning. Additionally, a conflict-adversarial module promotes segmentation consistency between different views. Finally, we propose a dynamic re-weighting strategy based on the effective number to improve attention to imbalanced classes. Experiments demonstrate that our method significantly improves segmentation performance, achieving state-of-the-art results on CT and MR images. Ablation studies further confirm the efficacy of boundary consistency constraints and dynamic re-weighting. The segmentation Dice score for minority organs (e.g., esophagus) on the Synapse dataset is improved by 17.1 %, 46.2 %, and 49.4 % using 10 %, 20 %, and 40 % labeled data, respectively.