Enhanced retinal arteries and veins segmentation through deep learning with conditional random fields

Abstract

The intricate network of retinal blood vessels serves as a sensitive window into systemic health, offering valuable insights into diseases like diabetic retinopathy. However, unraveling these insights poses challenges due to limitations of traditional visible light fundus photography. Infrared imaging emerges as a transformative tool, enabling deeper tissue penetration and enhanced visualization of the retinal vasculature. Yet, unlocking its full potential hinges on accurate and reliable segmentation of retinal arteries and veins within IR images. This study explores different ways to improve the accurate mapping of blood vessels in the eye using deep learning architectures. We used a special dataset captured with advanced technology to train and test three different models. This study amplifies the dataset’s adaptability, facilitating the training of U-Net, Residual U-Net, and Attention U-Net models. Among these models, the Attention Residual U-Net demonstrated superior segmentation performance, achieving an accuracy of 96.03%, a dice coefficient of 0.882, and a recall of 0.895 after post-processing. This research opens up possibilities for further improvements in eye-related healthcare.