Image segmentation of skin lesions based on dense atrous spatial pyramid pooling and attention mechanism

皮肤是人体最大的器官，很多内脏疾病会直接体现在皮肤上，准确分割皮肤病灶图像具有重要的临床意义。针对皮肤病灶区域颜色复杂、边界模糊、尺度信息参差不齐等特点，本文提出一种基于密集空洞空间金字塔池化（DenseASPP）和注意力机制的皮肤病灶图像分割方法。该方法以U型网络（U-Net）为基础，首先重新设计新的编码器，以大量残差连接代替普通的卷积堆叠，在拓展网络深度后还能有效保留关键特征；其次，将通道注意力与空间注意力融合并加入残差连接，从而使网络自适应地学习图像的通道与空间特征；最后，引入并重新设计的DenseASPP以扩大感受野尺寸并获取多尺度特征信息。本文所提算法在国际皮肤影像协会官方公开数据集（ISIC2016）中得到令人满意的结果，平均交并比（mIOU）、敏感度（SE）、精确率（PC）、准确率（ACC）和戴斯相似性系数（Dice）分别为0.901 8、0.945 9、0.948 7、0.968 1、0.947 3。实验结果证明，本文方法能够提高皮肤病灶图像分割效果，有望能为专业皮肤病医生提供辅助诊断。.; The skin is the largest organ of the human body, and many visceral diseases will be directly reflected on the skin, so it is of great clinical significance to accurately segment the skin lesion images. To address the characteristics of complex color, blurred boundaries, and uneven scale information, a skin lesion image segmentation method based on dense atrous spatial pyramid pooling (DenseASPP) and attention mechanism is proposed. The method is based on the U-shaped network (U-Net). Firstly, a new encoder is redesigned to replace the ordinary convolutional stacking with a large number of residual connections, which can effectively retain key features even after expanding the network depth. Secondly, channel attention is fused with spatial attention, and residual connections are added so that the network can adaptively learn channel and spatial features of images. Finally, the DenseASPP module is introduced and redesigned to expand the perceptual field size and obtain multi-scale feature information. The algorithm proposed in this paper has obtained satisfactory results in the official public dataset of the International Skin Imaging Collaboration (ISIC 2016). The mean Intersection over Union (mIOU), sensitivity (SE), precision (PC), accuracy (ACC), and Dice coefficient (Dice) are 0.901 8, 0.945 9, 0.948 7, 0.968 1, 0.947 3, respectively. The experimental results demonstrate that the method in this paper can improve the segmentation effect of skin lesion images, and is expected to provide an auxiliary diagnosis for professional dermatologists.