A review of generative adversarial network applications in optical coherence tomography image analysis

Jason Kugelman; David Alonso-Caneiro; Scott A. Read; Michael J. Collins

doi:10.1016/j.optom.2022.09.004

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A review of generative adversarial network applications in optical coherence tomography image analysis

Journal article

Open access

Peer reviewed

A review of generative adversarial network applications in optical coherence tomography image analysis

Jason Kugelman, David Alonso-Caneiro, Scott A. Read and Michael J. Collins

Journal of Optometry, Vol.15(Suppl 1), pp.S1-S11

2022

DOI: https://doi.org/10.1016/j.optom.2022.09.004

PMID: 36241526

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Abstract

optical coherence tomography

image analysis

deep learning

generative adversarial networks

Optical coherence tomography (OCT) has revolutionized ophthalmic clinical practice and research, as a result of the high-resolution images that the method is able to capture in a fast, non-invasive manner. Although clinicians can interpret OCT images qualitatively, the ability to quantitatively and automatically analyse these images represents a key goal for eye care by providing clinicians with immediate and relevant metrics to inform best clinical practice. The range of applications and methods to analyse OCT images is rich and rapidly expanding. With the advent of deep learning methods, the field has experienced significant progress with state-of-the-art-performance for several OCT image analysis tasks. Generative adversarial networks (GANs) represent a subfield of deep learning that allows for a range of novel applications not possible in most other deep learning methods, with the potential to provide more accurate and robust analyses. In this review, the progress in this field and clinical impact are reviewed and the potential future development of applications of GANs to OCT image processing are discussed. (c) 2022 Spanish General Council of Optometry. Published by Elsevier Espana, S.L.U. This is an open access article under the CC BY NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Details

Title: A review of generative adversarial network applications in optical coherence tomography image analysis
Authors: Jason Kugelman (Corresponding Author) - Queensland University of Technology
David Alonso-Caneiro (Author) - Queensland University of Technology
Scott A. Read (Author) - Queensland University of Technology
Michael J. Collins (Author) - Queensland University of Technology
Publication details: Journal of Optometry, Vol.15(Suppl 1), pp.S1-S11
Publisher: Colegio Nacional de Opticos Optometristas de Espana,Spanish Council of Optometry
Date published: 2022
DOI: 10.1016/j.optom.2022.09.004
ISSN: 1989-1342; 1888-4296
PMID: 36241526
Copyright note: © 2022 Spanish General Council of Optometry. Published by Elsevier España, S.L.U. This is an open access article under the CC BY NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 99972395102621
Output Type: Journal article

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