基于双注意力编码-解码器架构的视网膜血管分割

眼底视网膜血管的分割提取对于糖尿病、视网膜病、青光眼等眼科疾病的诊断具有重要的意义。针对视网膜血管图像中的血管难以提取、数据量较少等问题,文中提出了一种结合注意力模块和编码-解码器结构的视网膜血管分割方法。首先对编码-解码器卷积神经网络的每个卷积层添加空间和通道注意力模块,加强模型对图像特征的空间信息和通道信息(如血管的大小、形态和连通性等特点)的利用,从而改善视网膜血管的分割效果。其中,空间注意力模块关注于血管的拓扑结构特性,而通道注意力模块关注于血管像素点的正确分类。此外,在训练过程中采用Dice损失函数解决了视网膜血管图像正负样本不均衡的问题。在3个公开的眼底图像数据库DRIVE,STARE和CHASE_DB1上进行了实验,实验数据表明,所提算法的准确率、灵敏度、特异性和AUC值均优于已有的视网膜血管分割方法,其AUC值分别为0.9889,0.9812和0.9831。实验证明,所提算法能够有效提取健康视网膜图像和病变视网膜图像中的血管网络,能够较好地分割细小血管。

Retinal Vessel Segmentation Based on Dual Attention and Encoder-decoder Structure

The segmentation of the retinal vessels in fundus image is important for the diagnosis of ophthalmic diseases such as diabetes,retinopathy and glaucoma.Aiming at the difficulties of extracting blood vessels from retinal blood vessel images and the lack of data samples,a retinal vessel segmentation method combining attention module with encoder-decoder structure is proposed.To improve the segmentation effect of retinal blood vessels,a spatial and channel attention module is added to each convolutional layer of the encoder-decoder convolutional neural network to enhance the utilization of the spatial and channel information of the image features(such as the size,shape,and connectivity of the blood vessels),where the spatial attention focuses on the topological characteristics of blood vessels,and the channel attention focuses on the correct classification of blood vessel pixels.Moreover,the Dice loss function is used to solve the imbalance of positive and negative samples in retinal blood vessel images.The proposed method has been applied on three public fundus image databases DRIVE,STARE and CHASE_DB1.The experimental data show that the accuracy,sensitivity,specificity and AUC values are superior to the existing retinal vessel segmentation me-thods,with AUC values of 0.9889,0.9812 and 0.9831,respectively.The experimental results show that the proposed method can effectively extract the vascular network in healthy retinal images and diseased retinal images,and can segment small blood vessels well.