Improving U-Net architecture and graph cuts optimization to classify arterioles and venules in retina fundus images

Abstract

The central retinal artery and its branches supply blood to the inner retina. Vascular manifestations in the retina indirectly reflect the vascular changes and damage in organs such as the heart, kidneys, and brain because of the similar vascular structure of these organs. The diabetic retinopathy and risk of stroke are caused by increased venular caliber. The degrees of these diseases depend on the changes of arterioles and venules. The ratio between the calibers of arterioles and venules (AVR) is various. AVR is considered as the useful diagnostic indicator of different associated health problems. However, the task is not easy because of the lack of information of the features being used to classify the retinal vessels as arterioles and venules. This paper proposed a method to classify the retinal vessels into the arterioles and venules based on improving U-Net architecture and graph cuts. The accuracy of the proposed method is about 97.6%. The results of the proposed method are better than the other methods in RITE dataset and AVRDB dataset.

End-to-End Automatic Classification of Retinal Vessel Based on Generative Adversarial Networks with Improved U-Net

“…In order to improve the classification accuracy, the tracking algorithm was used as the post-processing method to further classify the blood vessels. Binh et al [ 30 ] also regarded arteriovenous classification as a three-classification task, the improved U-Net model was used to classify retinal vessels, and the method of graph cutting was used for post-processing; the accuracy of their method is about 97.6%.…”