BAF-UNet: a boundary-aware segmentation model for skin lesion segmentation

Purpose: Skin lesion segmentation plays a significant role in the diagnosis and treatment of skin cancer. Accurate skin lesion segmentation is essential for skin cancer diagnosis and treatment but is challenged by ambiguous boundaries and diverse lesion shapes and sizes. We aim to improve segmentation performance with enhanced boundary preservation. Approach: We propose BAF-UNet, a boundary-aware segmentation network. It integrates a multiscale boundary-aware feature fusion (BFF) module to combine low-level boundary features with high-level semantic information, and a boundary-aware vision transformer (BAViT) that incorporates boundary guidance into MobileViT to capture local and global context. A boundary-focused loss function is also introduced to prioritize edge accuracy during training. The model is evaluated on ISIC2016, ISIC2017, and PH2 datasets. Results: Experiments demonstrate that BAF-UNet improves Dice scores and boundary accuracy compared to baseline models. The BFF and BAViT modules enhance boundary delineation while maintaining robustness across lesions of varying shapes and sizes. Conclusions: BAF-UNet effectively integrates boundary guidance into feature fusion and transformer-based context modeling, significantly improving segmentation accuracy, particularly along lesion edges, and shows potential for clinical application in automated skin cancer diagnosis.