Accurate segmentation of skin lesions in dermatoscopic images is crucial for the early diagnosis of skin cancer and improving the survival rate of patients. However, it is still a challenging task due to the irregularity of lesion areas, the fuzziness of boundaries, and other complex interference factors. In this paper, a novel LCAUnet is proposed to improve the ability of complementary representation with fusion of edge and body features, which are often paid little attentions in traditional methods. First, two separate branches are set for edge and body segmentation with CNNs and Transformer based architecture respectively. Then, LCAF module is utilized to fuse feature maps of edge and body of the same level by local cross-attention operation in encoder stage. Furthermore, PGMF module is embedded for feature integration with prior guided multi-scale adaption. Comprehensive experiments on public available dataset ISIC 2017, ISIC 2018, and PH2 demonstrate that LCAUnet outperforms most state-of-the-art methods. The ablation studies also verify the effectiveness of the proposed fusion techniques.

翻译：皮肤镜图像中皮肤病变的精确分割对于皮肤癌的早期诊断及提升患者生存率至关重要。然而，由于病变区域的不规则性、边界模糊性以及其他复杂干扰因素，这一任务仍面临挑战。本文提出一种新型LCAUnet，通过融合边缘与体部特征来提升互补表征能力，而传统方法对此往往关注不足。首先，分别基于CNN和Transformer架构设置边缘分割与体部分割两个独立分支；其次，在编码阶段利用LCAF模块通过局部交叉注意力操作融合同一层级的边缘与体部特征图；此外，嵌入PGMF模块以先验引导的多尺度自适应机制实现特征整合。在公开数据集ISIC 2017、ISIC 2018及PH2上的综合实验表明，LCAUnet性能优于多数现有最优方法。消融研究也验证了所提融合技术的有效性。