Multi-scale and Boundary Fusion Network for Skin Lesion Regions Segmentation

Accurate segmentation of skin lesion regions is a key step in clinical diagnosis and analysis. Aiming at the poor segmentation effect of the existing networks in skin lesion regions due to the presence of variable size, irregular shape, fuzzy boundaries and obscured lesion regions, a multi-scale and boundary fusion network (MSBF-Net) for skin lesion region segmentation is proposed by improving the original structure based on the U-Net. Specifically, firstly, a split pooling (SplitPool) module is proposed, which effectively solves the problem of spatial information loss while reducing the image resolution. Secondly, a full-scale feature fusion (FSFF) module is proposed, which effectively solves the problem that the previous methods only fuse the deep features to the shallow features, while ignoring the contribution of the detail information in the more shallow features to the network segmentation decision. Meanwhile, the original jump connections of U-Net are redesigned to provide richer contextual information for the decoder. Finally, sub-paths for enhancing the network’s ability to learn boundary features are proposed, and the boundary fusion (BF) module is introduced to fuse the prediction results of the main paths and sub-paths, which effectively solves the problems of irregular shape and boundary ambiguity of the lesion region. Dice and JI reach 90.12% and 83.61% on the ISIC2018 dataset, which are 1.13 percentage points and 1.62 percentage points higher than the baseline network, respectively; Dice and JI reach 94.72% and 90.18% on the PH2 dataset, which are 1.49 percentage points and 2.17 percentage points higher than the baseline network, respectively. Experimental results show that MSBF-Net significantly improves the accuracy of skin lesion region segmentation and exceeds the existing state-of-the-art methods in several indices, further validating the effectiveness of the method.