Retinal images play an essential role in the early diagnosis of ophthalmic diseases. Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background. At the same time, automated models struggle to capture representative and discriminative retinal vascular features. To fully utilize the structural information of the retinal blood vessels, we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network (PCRTAM-Net). PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network. In addition, the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow. A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies. We evaluate the proposed PCRTAM-Net on four publicly available datasets, DRIVE, CHASE_DB1, STARE, and HRF. Our model achieves state-of-the-art performance of 97.10%, 97.70%, 97.68%, and 97.14% for ACC and 83.05%, 82.26%, 84.64%, and 81.16% for F1, respectively.

视网膜图像在眼科疾病的早期诊断中发挥着重要作用。由于视网膜血管和低对比度背景之间的形态差异，彩色眼底图像中视网膜血管的自动分割具有挑战性。与此同时，自动化模型难以捕捉具有代表性和辨别力的视网膜血管特征。为了充分利用视网膜血管的结构信息，我们提出了一种新型深度学习网络，称为预激活卷积残差和三重注意力机制网络（PCRTAM-Net）。PCRTAM-Net采用预激活dropout卷积残差方法来提高网络的特征学习能力。此外，网络编码器两端集成残差空洞卷积空间金字塔，提取多尺度信息，改善血管信息流。提出了三重注意机制来提取血管上下文之间的结构信息并学习远程特征依赖性。我们在四个公开可用的数据集 DRIVE、CHASE_DB1、STARE 和 HRF 上评估了所提出的 PCRTAM-Net。我们的模型在 ACC 方面实现了 97.10%、97.70%、97.68% 和 97.14% 的最佳性能，在 ACC 方面实现了 83.05%、82.26%、84.64% 和 81.16% 的最佳性能 凝视和 HRF。我们的模型在 ACC 方面实现了 97.10%、97.70%、97.68% 和 97.14% 的最佳性能，在 ACC 方面实现了 83.05%、82.26%、84.64% 和 81.16% 的最佳性能 凝视和 HRF。我们的模型在 ACC 方面实现了 97.10%、97.70%、97.68% 和 97.14% 的最佳性能，在 ACC 方面实现了 83.05%、82.26%、84.64% 和 81.16% 的最佳性能分别为F 1 。