A Boundary‐Guided and Lesion‐Consistent CNN ‐Transformer Network for Breast Ultrasound Lesion Segmentation

ABSTRACT

Accurate delineation of breast lesions in ultrasound images can support reproducible lesion measurement and quantitative analysis after lesion presence has been established. Although convolutional neural networks (CNNs) have achieved promising performance in medical image segmentation, their limited receptive fields hinder long‐range contextual modeling. Transformer‐based methods address this limitation through self‐attention. However, conventional dense attention often introduces redundant token interactions and remains susceptible to ambiguous background responses in BUS images. In addition, generic feed‐forward transformations do not explicitly promote spatial consistency within lesion regions. To address these challenges, we propose BGLC‐Net, a boundary‐guided and lesion‐consistent CNN‐Transformer network for BUS lesion segmentation. Its core module, the Lesion‐Guided Transformer Block (LGTB), is embedded in each encoding stage and comprises Boundary‐Guided Token Selection (BGTS) attention and a Lesion‐Region Consistency Feed‐Forward Network (LRC‐FFN). BGTS incorporates boundary‐related guidance to emphasize contour‐informative responses while suppressing background interactions. LRC‐FFN strengthens lesion representation through progressive multi‐scale contextual modeling, improving lesion‐region consistency across multiple contextual scales. Experiments on the BUSI dataset show that BGLC‐Net achieves a Dice score of 81.02%, a Jaccard index of 73.12%, and an HD95 of 8.84. Overall, these results are superior or competitive compared with those of representative BUS segmentation methods. External validation on the independent STU dataset further suggests preliminary cross‐dataset generalization under dataset‐level appearance shifts, indicating that BGLC‐Net may provide a useful technical basis for reproducible BUS lesion segmentation.