Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks.

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.