Medical image segmentation is essential for clinical decision-making, treatment planning, and disease monitoring. However, ambiguous boundaries and complex anatomical structures continue to pose challenges for accurate segmentation. To address these issues, we propose DCANet (Disentangled and Category-Aware Network), a novel framework that effectively integrates local and global feature representations while enhancing category-aware feature interactions. In DCANet, features from convolutional and Transformer layers are fused using the Feature Coupling Unit (FCU), which aligns and combines local and global information across multiple semantic levels. The Decoupled Feature Module (DFM) then separates high-level representations into multi-class foreground and background features, improving discriminability and mitigating boundary ambiguity. Finally, the Category-Aware Integration Aggregator (CAIA) guides multi-level feature fusion, emphasizes critical regions, and refines segmentation boundaries. Extensive experiments on four public datasets—Synapse, ACDC, GlaS, and MoNuSeg—demonstrate the superior performance of DCANet, achieving Dice scores of 84.80%, 94.07%, 94.60%, and 79.85%, respectively. These results confirm the effectiveness and generalizability of DCANet in accurately segmenting complex anatomical structures and resolving boundary ambiguities across diverse medical image segmentation tasks.
Luo et al. (Tue,) studied this question.