ReLayer: a Free, Online Tool for Extracting Retinal Thickness From Cross-Platform OCT Images

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Abstract

Purpose

To describe and evaluate a free, online tool for automatically segmenting optical coherence tomography (OCT) images from different devices and computing summary measures such as retinal thickness.

Methods

ReLayer ( https://relayer.online) is an online platform to which OCT scan images can be uploaded and analyzed. Results can be downloaded as plaintext (.csv) files. The segmentation method includes a novel, one-dimensional active contour model, designed to locate the inner limiting membrane, inner/outer segment, and retinal pigment epithelium. The method, designed for B-scans from Heidelberg Engineering Spectralis, was adapted for Topcon 3D OCT-2000 and OptoVue AngioVue. The method was applied to scans from healthy and pathological eyes, and was validated against segmentation by the manufacturers, the IOWA Reference Algorithms, and manual segmentation.

Results

Segmentation of a B-scan took ≤1 second. In healthy eyes, mean difference in retinal thickness from ReLayer and the reference standard was below the resolution of the Spectralis and 3D OCT-2000, and slightly above the resolution of the AngioVue. In pathological eyes, ReLayer performed similarly to IOWA ( P = 0.97) and better than Spectralis ( P < 0.001).

Conclusions

A free online platform (ReLayer) is capable of segmenting OCT scans with similar speed, accuracy, and reliability as the other tested algorithms, but offers greater accessibility. ReLayer could represent a valuable tool for researchers requiring the full segmentation, often not made available by commercial software.

Translational Relevance

A free online platform (ReLayer) provides free, accessible segmentation of OCT images: data often not available via existing commercial software.

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Most cited references 20

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Retinal imaging and image analysis.

Michael D. Abramoff, Milan Sonka, Mona K. Garvin (2009)

Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.

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Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.

Leyuan Fang, David Cunefare, Chong Wang … (2017)

We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.

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Is Open Access

The Development, Commercialization, and Impact of Optical Coherence Tomography

James Fujimoto, Eric Swanson (2016)

This review was written for the special issue of IOVS to describe the history of optical coherence tomography (OCT) and its evolution from a nonscientific, historic perspective. Optical coherence tomography has become a standard of care in ophthalmology, providing real-time information on structure and function – diagnosing disease, evaluating progression, and assessing response to therapy, as well as helping to understand disease pathogenesis and create new therapies. Optical coherence tomography also has applications in multiple clinical specialties, fundamental research, and manufacturing. We review the early history of OCT describing how research and development evolves and the important role of multidisciplinary collaboration and expertise. Optical coherence tomography had its origin in femtosecond optics, but used optical communications technologies and required advanced engineering for early OCT prototypes, clinical feasibility studies, entrepreneurship, and corporate development in order to achieve clinical acceptance and clinical impact. Critical advances were made by early career researchers, clinician scientists, engineering experts, and business leaders, which enabled OCT to have a worldwide impact on health care. We introduce the concept of an “ecosystem” consisting of research, government funding, collaboration and competition, clinical studies, innovation, entrepreneurship and industry, and impact – all of which must work synergistically. The process that we recount is long and challenging, but it is our hope that it might inspire early career professionals in science, engineering, and medicine, and that the clinical and research community will find this review of interest.

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Author and article information

Journal

Journal ID (nlm-ta): Transl Vis Sci Technol

Journal ID (iso-abbrev): Transl Vis Sci Technol

Journal ID (hwp): tvst

Journal ID (pmc): Transl Vis Sci Technol

Journal ID (publisher-id): TVST

Title: Translational Vision Science & Technology

Publisher: The Association for Research in Vision and Ophthalmology

ISSN (Electronic): 2164-2591

Publication date Collection: May 2019

Publication date (Electronic): 29 May 2019

Volume: 8

Issue: 3

Electronic Location Identifier: 25

Affiliations

[1 ]Division of Optometry and Visual Science, School of Health Sciences, City, University of London, London, UK

[2 ]UCL Cancer Institute, University College London, London, UK

[3 ]University of Milan, School of Ophthalmology, Milan, Italy

[4 ]Moorfields Eye Hospital, London, UK

[5 ]School of Computer Science, University of Lincoln, Lincoln, UK

[6 ]Institute of Ophthalmology, University College London, London, UK

[7 ]NIHR Biomedical Research Centre (Moorfields Eye Hospital NHS Foundation Trust/University College London), London, UK

[8 ]Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

[9 ]Centre for Translational Inflammation Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK

Author notes

Correspondence: Giovanni Ometto, Division of Optometry and Visual Science, School of Health Sciences, City, University of London, Northampton Square, Clerkenwell, London EC1V 0HB, UK. e-mail: giovanni.ometto@ 123456city.ac.uk

Article

Serial Item and Contribution ID: tvst-08-03-13 Publisher ID: TVST-18-1065

DOI: 10.1167/tvst.8.3.25

PMC ID: 6543924

SO-VID: 19adfbdc-b08e-4639-973e-dbcb2feadc30

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

ReLayer: a Free, Online Tool for Extracting Retinal Thickness From Cross-Platform OCT Images

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Abstract

Purpose

Methods

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Translational Relevance

Related collections

Karger: Ophthalmology

Most cited references 20

Retinal imaging and image analysis.

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.

The Development, Commercialization, and Impact of Optical Coherence Tomography

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