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Optic Disc and Fovea Localisation in Ultra-widefield Scanning Laser Ophthalmoscope Images Captured in Multiple Modalities

Wakeford, P. R. , Pellegrini, E., Robertson, G., Verhoek, M., Fleming, A. D., van Hemert, J. and Heng, I. S. (2020) Optic Disc and Fovea Localisation in Ultra-widefield Scanning Laser Ophthalmoscope Images Captured in Multiple Modalities. In: MIUA 2019, Liverpool, United Kingdom, 24-26 July 2019, pp. 399-410. ISBN 9783030393427 (doi: 10.1007/978-3-030-39343-4_34)

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Abstract

We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina. Images captured in both reflectance and autofluorescence (AF) modes, and central pole and eyesteered gazes, were used. The method achieved an OD localisation accuracy of 99.4% within one OD radius, and fovea localisation accuracy of 99.1% within one OD radius on a test set comprising of 1790 images. The performance of fovea localisation in AF images was comparable to the variation between human annotators at this task. The laterality of the image (whether the image is of the left or right eye) was inferred from the OD and fovea coordinates with an accuracy of 99.9%.

Item Type:	Conference Proceedings
Keywords:	Optic disc detection, fovea detection, laterality determination, retinal images, convolutional neural networks.
Status:	Published
Refereed:	Yes
Glasgow Author(s) Enlighten ID:	Wakeford, Peter and Heng, Professor Ik Siong
Authors:	Wakeford, P. R., Pellegrini, E., Robertson, G., Verhoek, M., Fleming, A. D., van Hemert, J., and Heng, I. S.
Subjects:	Q Science > QC Physics R Medicine > RE Ophthalmology
College/School:	College of Science and Engineering > School of Physics and Astronomy
Research Centre:	College of Science and Engineering > School of Physics and Astronomy > Institute for Gravitational Research
Research Group:	Institute for Gravitational Research
ISSN:	1865-0929
ISBN:	9783030393427
Published Online:	24 January 2020
Copyright Holders:	Copyright © 2020 Springer Nature
First Published:	First published in Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings: 399-410
Publisher Policy:	Reproduced in accordance with the publisher copyright policy
Related URLs:	Organisation

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References

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Deposit and Record Details

ID Code:	209036
Depositing User:	Dr Jamie Scott
Datestamp:	30 Jan 2020 08:48
Last Modified:	06 Feb 2023 16:41
Date of acceptance:	15 April 2019
Date of first online publication:	24 January 2020
Date Deposited:	29 January 2020
Data Availability Statement:	No