BSP-Net: automatic skin lesion segmentation improved by boundary enhancement and progressive decoding methods

Abstract

Automatic skin lesion segmentation from dermoscopy images is of great significance in the early treatment of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. In this paper, we propose a network BSP-Net that implements the combination of critical boundary information and segmentation tasks to simultaneously solve the variation and boundary problems in skin lesion segmentation. The architecture of BSP-Net primarily consists of a multi-scale boundary enhancement (MBE) module and a progressive fusion decoder (PD). The MBE module, by deeply extracting boundary information in both multi-axis frequency and multi-scale spatial domains, generates precise boundary key-point prediction maps. This process not only accurately models local boundary information but also effectively retains global contextual information. On the other hand, the PD employs an asymmetric decoding strategy, guiding the generation of refined segmentation results by combining boundary-enhanced features rich in geometric details with global features containing semantic information about lesions. This strategy progressively fuses boundary and semantic information at different levels, effectively enabling high-performance collaboration between cross-level contextual features. To assess the effectiveness of BSP-Net, we conducted extensive experiments on two public datasets (ISIC-2016 &PH2, ISIC-2018) and one private dataset (XJUSKin). BSP-Net achieved Dice coefficients of 90.81, 92.41, and 83.88%, respectively. Additionally, it demonstrated precise boundary delineation with Average Symmetric Surface Distance (ASSD) scores of 7.96, 6.88, and 10.92%, highlighting its strong performance in skin lesion segmentation.