边缘增强结合多尺度信息融合的皮肤病变分割

Skin Lesion Segmentation Based on Edge Enhancement Combined with Multi-scale Information Fusion

针对皮肤病灶大小不一、与周围皮肤对比度低、边界模糊不规则、存在伪影及毛发干扰等问题,本文提出一种边缘增强结合多尺度信息融合的皮肤病变分割算法,该算法由编码器、多尺度感知模块、边缘增强模块、轻量化解码器组成.首先在编码器中构建Transformer模块以提取全局信息,使用卷积操作以提取局部信息;其次设计多尺度感知模块,采用密集连接结构的门控空洞卷积金字塔模块来融合多尺度特征;接着构建边缘增强模块,利用深层特征促进对边缘特征的探索以更好的保留细节和边缘信息;最后设计轻量化解码器,采用CARAFE轻量化算子进行上采样,在使用相对较少参数的情况下保持较高的分割精度.在公开数据集ISIC2016和ISIC2018上做对比实验,结果表明,本文算法分割精度高于其他流行算法.

To address the problems of skin lesions,such as varied sizes,low contrast with surrounding skin,blurred and irregular boundaries,artifacts,and hair interference,this study proposes a skin lesion segmentation algorithm that combines edge enhancement with multi-scale information fusion.The algorithm consists of an encoder,a multi-scale sensing module,an edge enhancement module,and a lightweight decoder.Firstly,a Transformer module is built in the encoder to extract global information,and convolution operations are used to extract local information.Secondly,a multi-scale sensing module is designed to integrate multi-scale features using a gated atrous convolution pyramid module with a dense connection structure.An edge enhancement module is constructed,utilizing deep features to promote the exploration of edge features to better retain details and edge information.Finally,a lightweight decoder is designed,employing the CARAFE lightweight operator for upsampling,to maintain high segmentation accuracy with fewer parameters.Comparative experiments on open data sets ISIC2016 and ISIC2018 show that the segmentation accuracy of the proposed algorithm is higher than that of other popular algorithms.