Accurate medical image segmentation is crucial for clinical diagnosis and treatment planning. However, class imbalance and vagueness of boundary in medical images make it challenging to achieve accurate and reliable results. In particular, 3D multi-organ segmentation is a complex process. These challenges are further exacerbated in semi-supervised learning settings with limited labeled data. Existing methods rarely effectively incorporate boundary information to mitigate class imbalance, leading to biased predictions and suboptimal segmentation accuracy. To address these limitations, we propose DBANet, a dual-model framework integrating three key modules. The Confidence-Guided Pseudo-Label Fusion (CPF) module enhances pseudo-label reliability by selecting high-confidence logits. This improves training stability in limited annotation settings. The Boundary Distribution Awareness (BDA) module dynamically adjusts class weights based on boundary distributions, mitigating class imbalance and enhancing segmentation performance. Additionally, the Boundary Vagueness Awareness (BVA) module further refines boundary delineation by prioritizing regions with blurred boundaries. Experiments on two benchmark datasets validate the effectiveness of DBANet. On the Synapse dataset, DBANet achieves average Dice score improvements of 3.56%, 2.17%, and 5.12% under 10%, 20%, and 40% labeled data settings, respectively. Similarly, on the WORD dataset, DBANet achieves average Dice score improvements of 1.72%, 0.97%, and 0.65% under 2%, 5%, and 10% labeled data settings, respectively. These results highlight the potential of boundary-aware adaptive weighting for advancing semi-supervised medical image segmentation.
Chen et al. (Wed,) studied this question.