Retinal blood vessels are the only deep microvessels in the blood circulation system that can be observed directly and noninvasively, providing us with a means of observing vascular pathologies. Cardiovascular and cerebrovascular diseases, such as glaucoma and diabetes, can cause structural changes in the retinal microvascular network. Therefore, the study of effective retinal vessel segmentation methods is of great significance for the early diagnosis of cardiovascular diseases and the vascular network’s quantitative results. This paper proposes an automatic retinal vessel segmentation method based on an improved U-Net network. Firstly, the image patches are rotated to amplify the image data, and then, the RGB fundus image is preprocessed by normalization. Secondly, after the improved U-Net model is constructed with 23 convolutional layers, 4 pooling layers, 4 upsampling layers, 2 dropout layers, and Squeeze and Excitation (SE) block, the extracted image patches are utilized for training the model. Finally, the fundus images are segmented through the trained model to achieve precise extraction of retinal blood vessels. According to experimental results, the accuracy of 0.9701, 0.9683, and 0.9698, sensitivity of 0.8011, 0.6329, and 0.7478, specificity of 0.9849, 0.9967, and 0.9895, F1-Score of 0.8099, 0.8049, and 0.8013, and area under the curve (AUC) of 0.8895, 0.8845, and 0.8686 were achieved on DRIVE, STARE, and HRF databases, respectively, which is better than most classical algorithms.

中文翻译：

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基于改进的U-Net方法的视网膜血管自动分割

视网膜血管是血液循环系统中唯一可以直接和无创观察到的深层微血管，为我们提供了一种观察血管病变的方法。诸如青光眼和糖尿病之类的心血管和脑血管疾病会引起视网膜微血管网络的结构变化。因此，研究有效的视网膜血管分割方法对心血管疾病的早期诊断和血管网络的定量结果具有重要意义。提出了一种基于改进的U-Net网络的视网膜血管自动分割方法。首先，旋转图像斑块以放大图像数据，然后通过标准化对RGB眼底图像进行预处理。其次，在用23个卷积层构建改进的U-Net模型之后，4个池化层，4个上采样层，2个滤除层和挤压和激发（SE）块，提取的图像块用于训练模型。最后，通过训练的模型对眼底图像进行分割，以实现视网膜血管的精确提取。根据实验结果，精度为0.9701、0.9683和0.9698，灵敏度为0.8011、0.6329和0.7478，特异性为0.9849、0.9967和0.9895，分别在DRIVE，STARE和HRF数据库上获得0.899、0.8049和0.8013的F 1得分以及0.8895、0.8845和0.8686的曲线下面积（AUC），这比大多数经典算法要好。