Retinal image contains rich information on the blood vessel and is highly related to vascular diseases. Fully automatic and accurate identification of arteries and veins from the complex background of retinal images is essential for analyzing eye-relevant diseases, and monitoring progressive eye diseases. However, popular methods, including deep learning-based models, performed unsatisfactorily in preserving the connectivity of both the arteries and veins. The results were shown to be disconnected or overlapped by the twos and thus manual calibration was needed to refine the results. To tackle the problem, this paper proposes a topological structure-constrained generative adversarial network (topGAN) to automatically identify and differentiate the arteries and veins from retinal images. The introduced topological structure term can automatically delineate the topological structure properties of retinal blood vessels and greatly improves the vascular connectivity of the entire arteriovenous classification results. We train and evaluate our model on both the AV-DRIVE public available dataset and the CVDG home-owned dataset, which consists of 40 images and 3119 images, respectively. Experiments demonstrate that integrating topological structure constraints can significantly improve the performance of arteriovenous classification. Our method achieves excellent performance with an accuracy of 94.3% on the AV-DRIVE dataset and 93.6% on the CVDG dataset.

视网膜图像包含丰富的血管信息，并且与血管疾病高度相关。从复杂的视网膜图像背景中自动，准确地识别动脉和静脉对于分析与眼有关的疾病和监测进行性眼病至关重要。但是，包括基于深度学习的模型在内的流行方法在保持动脉和静脉的连通性方面效果不理想。结果表明结果是不连贯的或重叠的，因此需要手动校准来完善结果。为了解决这个问题，本文提出了一种拓扑结构受限的生成对抗网络（topGAN），以自动识别和区分视网膜图像中的动脉和静脉。引入的拓扑结构术语可以自动描绘视网膜血管的拓扑结构特性，并极大地改善整个动静脉分类结果的血管连通性。我们在AV-DRIVE公开数据集和CVDG私有数据集上训练和评估模型，该数据集分别由40张图像和3119张图像组成。实验表明，整合拓扑结构约束可以显着改善动静脉分类的性能。我们的方法具有出色的性能，在AV-DRIVE数据集上的准确度为94.3％，在CVDG数据集上的准确度为93.6％。我们在AV-DRIVE公开数据集和CVDG私有数据集上训练和评估模型，该数据集分别由40张图像和3119张图像组成。实验表明，整合拓扑结构约束可以显着改善动静脉分类的性能。我们的方法具有出色的性能，在AV-DRIVE数据集上的准确度为94.3％，在CVDG数据集上的准确度为93.6％。我们在AV-DRIVE公开数据集和CVDG私有数据集上训练和评估模型，该数据集分别由40张图像和3119张图像组成。实验表明，整合拓扑结构约束可以显着改善动静脉分类的性能。我们的方法具有出色的性能，在AV-DRIVE数据集上的准确度为94.3％，在CVDG数据集上的准确度为93.6％。