基于多尺度特征融合双U型皮肤病变分割算法

针对皮肤病分割问题中皮肤病变区域大小不一且形状各异问题,提出一种基于多尺度特征融合的双U型皮肤病分割算法.该算法由粗分U型网络和细分U型网络两部分组成.首先粗分U型网络编码部分采用预训练VGG-19模型对相关特征进行多尺度特征提取;在解码阶段利用改进注意力残差块将底层与高层信息进行有效的映射融合,得到初步的Mask;然后将初步生成的Mask与原图像聚合,并输入多路特征提取编码器中进行二次特征蒸馏;而细分U型网络解码器同时与粗分U型网络编码部分和细分U型网络的编码部分特征映射进行融合,保证网络可以聚合更多的有效特征;最后利用Focal Tversky损失函数进一步提升分割效果.实验表明,所提算法在ISBI2016数据集上实验分割精度为96.11％、敏感度为93.59％、特异性为97.10％、Dice系数为93.14％、Jaccard系数为87.17％,能够有效地分割皮肤病病变区域.

Skin lesion image segmentation algorithm based on multi-scale feature fusion double U-Net

Aiming at the problem of different sizes and different shapes of skin lesions in skin disease segmentation, this paper proposed a double U-shaped skin disease segmentation algorithm based on multi-scale feature fusion. The algorithm consisted of two parts: coarse U-shaped network and subdivided U-shaped network. Firstly, the coarse U-shaped network encoder used the pre-trained VGG-19 model to extract multi-scale features for related features. In the decoder stage, the improved attention residual block effectively mapped and merged the low-level and high-level information to obtain a preliminary Mask. Then it aggregated the preliminary Mask with the original image, and input it into the multi-path feature extraction encoder for secondary feature distillation. The subdivided U-shaped network decoder simultaneously integrated with the feature mapping of the coarse U-shaped network encoder and the subdivided U-shaped network encoder to ensure that the network could aggregate more effective features. Finally, the Focal Tversky loss function further improved the segmentation effect. Experimental results on the ISBI 2016 dataset show that the accuracy, sensitivity, specificity, Dice coefficient and Jaccard coefficient of the proposed method are 96.11%, 93.59%, 97.10%, 93.14% and 87.17%, respectively, which can effectively segment the skin disease area.