A Review of Machine Learning Methods for Retinal Blood Vessel Segmentation and Artery/Vein Classification

Muthu Rama Krishnan Mookiah (Lead / Corresponding author), Stephen Hogg, Tom J. MacGillivray, Vijayaraghavan Prathiba, Rajendra Pradeepa, Viswanathan Mohan, Ranjit Mohan Anjana, Alexander S. Doney, Colin N. A. Palmer, Emanuele Trucco

Research output: Contribution to journalReview articlepeer-review

Abstract

The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification 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 classification for fundus camera images. We divide the methods into various classes by task (segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific 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 offer our views on the outlook for vessel segmentation and classification for fundus camera images.

Original languageEnglish
Article number101905
Number of pages26
JournalMedical Image Analysis
Volume68
Early online date17 Nov 2020
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Deep learning
  • Machine learning
  • Medical imaging
  • Retinal vessels
  • Review
  • Segmentation

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