DPGNet: A Boundary-Aware Medical Image Segmentation Framework via Uncertainty Perception

Addressing the critical challenge of precise boundary delineation in medical image segmentation, we introduce DPGNet, an adaptive deep learning model engineered to emulate expert perception of intricate anatomical edges. Our key innovations drive its superior performance and clinical utility, encompassing: 1) a three-stage progressive refinement strategy that establishes global context, performs hierarchical feature enhancement, and precisely delineates local boundaries; 2) a novel Edge Difference Attention (EDA) module that implicitly learns and quantifies boundary uncertainties without requiring explicit ground truth supervision; and 3) a lightweight, transformer-based architecture ensuring an exceptional balance between performance and computational efficiency. Extensive experiments across diverse and challenging medical image datasets demonstrate DPGNet's consistent superiority over state-of-the-art methods, notably achieving this with significantly lower computational overhead (25.51 M parameters). Its exceptional boundary refinement is rigorously validated through comprehensive metrics (Boundary-IoU, HD95) and confirmed by rigorous clinical expert evaluations. Crucially, DPGNet generates an explicit uncertainty boundary map, providing clinicians with actionable insights to identify ambiguous regions, thereby enhancing diagnostic precision and facilitating more accurate clinical segmentation outcomes.