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Accurate segmentation of skin lesions in dermatoscopic images is crucial forthe early diagnosis of skin cancer and improving the survival rate of patients.However, it is still a challenging task due to the irregularity of lesionareas, the fuzziness of boundaries, and other complex interference factors. Inthis paper, a novel LCAUnet is proposed to improve the ability of complementaryrepresentation with fusion of edge and body features, which are often paidlittle attentions in traditional methods. First, two separate branches are setfor edge and body segmentation with CNNs and Transformer based architecturerespectively. Then, LCAF module is utilized to fuse feature maps of edge andbody of the same level by local cross-attention operation in encoder stage.Furthermore, PGMF module is embedded for feature integration with prior guidedmulti-scale adaption. Comprehensive experiments on public available datasetISIC 2017, ISIC 2018, and PH2 demonstrate that LCAUnet outperforms moststate-of-the-art methods. The ablation studies also verify the effectiveness ofthe proposed fusion techniques.

LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion