Boundary-enhanced sparse transformer for generalizable and accurate medical image segmentation.

Medical image segmentation is a fundamental task in computer-aided diagnosis, playing a crucial role in organ structure analysis, lesion delineation, and treatment planning. However, current Transformer-based segmentation networks still face two major challenges. First, the global self-attention in the encoder often introduces redundant connections, leading to high computational cost and potential interference from irrelevant tokens. Second, the decoder shows limited capability in reconstructing fine-grained boundary structures, resulting in blurred segmentation contours. To address these issues, we proposed an efficient and accurate framework for general medical image segmentation. Specifically, in the encoder, we introduce a frequency-domain similarity measure and construct a Key-Semantic Dictionary (KSD) via amplitude spectrum cosine similarity. This enables stage-wise sparse attention matrices that reduce redundancy and enhance semantic relevance. In the decoder, we design a learnable gradient-based operator that injects boundary-aware logits bias into the attention mechanism, thereby improving structural detail recovery along object boundaries. On ACDC, the framework delivers a 0.55% gain in average Dice and a 14.6% reduction in HD over the second-best baseline. On ISIC 2018, it achieves increases of 1.01% in Dice and 0.21% in ACC over the second-best baseline, while using 88.8% fewer parameters than typical Transformer-based models. On Synapse, it surpasses the strongest prior approach by 1.03% in Dice and 6.35% in HD, yielding up to 8.36% Dice improvement and 52.46% HD reduction compared with widely adopted Transformer baselines. Comprehensive results confirm that the proposed frequency-domain sparse attention and learnable edge-guided decoding effectively balance segmentation accuracy, boundary fidelity, and computational cost. This framework not only suppresses redundant global correlations and enhances structural detail reconstruction, but is also robust to different medical imaging modalities, providing a lightweight and clinically applicable solution for high-precision medical image segmentation.