A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

Abstract

The application of new techniques in eye imaging has revolutionized the process by increasing accuracy and efficiency levels considerably. An essential aspect of analyzing ocular images is the correct segmentation of retinal vessels. Deep learning algorithms represent an effective solution giving high accuracy rates for this task. In this article readers can find an extensive overview of several novel approaches built specifically to address this issue-among them being a groundbreaking deep learning architecture that applies multi-scale/multi-level Convolutional Neural Networks (CNNs) to obtain a comprehensive hierarchical representation of retinal vessels. Furthermore, advanced approaches are also discussed on detecting retinal vessels from Fluorescein Angiography (FA) images -with notable examples being one that combines cross modality transfer with human in the loop learning helping reduce manual labelling efforts significantly while enhancing both accuracy and performance metrics simultaneously. These stateof-the-art methods unravel critical insights into how deep learning methods could improve retinal vessel segmentation. The impact they could have on diagnosing and treating various eye related illnesses is also noteworthy.