A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification

Abstract

The eye aords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classication are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic vessel segmentation and classication for fundus camera images. We divide the methods into various classes by task (segmentation or artery-vein classication), technique class (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specic algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to oer our views on the outlook for vessel segmentation and classication for fundus camera images.