【摘要】 为解决现有眼底图像分割方法对于细微血管存在低分割精度和低准确率的问题，提出一种基于编解码结构的U-Net改进网络模型。首先对数据进行预处理与扩充，提取绿色通道图像，并将其通过对比度限制直方图均衡化和伽马变换以增强对比度；其次训练集被输入到用于分割的神经网络中，在编码过程加入残差模块，用短跳跃连接将高、低特征信息融合，并利用空洞卷积增加感受野，解码模块加入注意力机制增加对细微血管分割精度；最后利用训练完成的分割模型进行预测得出视网膜血管分割结果。在DRIVE和CHASE-DB1眼底图像数据集上进行对比实验，模型算法的平均准确率、特异性和灵敏度分别达到96.77%和97.22%、98.74%和98.40%、80.93%和81.12%。实验结果表明该算法能够改善微细血管分割准确率及效率不高的问题，对视网膜血管可以进行更准确的分割。更多还原

【Abstract】 In order to solve the problem that the existing fundus images segmentation methods have low segmentation precision and low accuracy for micro vessels, an improved U-Net network model based on codec structure is proposed. Firstly, the data is preprocessed and expanded, the green channel image is extracted, and the contrast is enhanced by contrasting limited histogram equalization and Gamma transform; Secondly, the training set is input into the neural network for segmentation, the residual module is added in the coding process, the high and low feature information are fused by short jump connection, the receptive field is increased by hole convolution, and the attention mechanism is added in the decoding module to increase the segmentation accuracy of fine blood vessels; Finally, the trained segmentation model is used to predict the retinal vascular segmentation results. Comparative experiments on DRIVE and CHASE-DB1 fundus image data sets show that the average accuracy, specificity and sensitivity of the model algorithm are 96. 77% and 97. 22%, 98. 74% and 98. 40%, 80. 93% and 81. 12% respectively. The results of experiments show that the algorithm can improve the accuracy and efficiency of microvascular segmentation, and can segment retinal vessels more accurately.更多还原