SBEM-UNet: A Semantic Boundary and Contour-Enhanced Framework for Semisupervised Medical Image Segmentation

In medical image segmentation, inherent boundary ambiguity, tissue overlap, and weak intensity gradients often produce blurred or discontinuous edges, posing persistent challenges to accurate anatomical delineation. Although deep learning approaches have advanced segmentation performance, their capacity to model ambiguous boundaries remains limited, especially under scarce annotation conditions. To address this, we propose SBEM-UNet, a novel semisupervised learning framework that explicitly targets boundary blurring and discontinuity through two complementary components: the semantic boundary enhancement module (SBEM) and the contour enhancement decoder (CED). SBEM leverages multiscale semantic aggregation with attention mechanisms to enhance structural discriminability and semantic coherence, mitigating boundary-induced uncertainty and preserving fine-grained details, while CED performs fine-grained contour modeling via dynamic boundary extraction and adaptive global feature modulation, enabling precise localization of blurred or discontinuous anatomical edges. To further improve robustness under limited annotation, the framework incorporates pseudolabel consistency regularization within the semisupervised learning paradigm, facilitating effective feature self-calibration and generalization across diverse imaging conditions. Experiments on public benchmarks demonstrate that SBEM-UNet consistently surpasses existing semisupervised methods in both region-based accuracy and boundary delineation quality, highlighting its effectiveness and practical value in low-annotation clinical scenarios.