Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images

Abstract High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical research and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo...

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Autores principales:	Laura K. Young, Hannah E. Smithson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8c9fa2db5ee640198e779001bce947a0
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spelling	oai:doaj.org-article:8c9fa2db5ee640198e779001bce947a02021-12-02T16:53:02ZEmulated retinal image capture (ERICA) to test, train and validate processing of retinal images10.1038/s41598-021-90389-y2045-2322https://doaj.org/article/8c9fa2db5ee640198e779001bce947a02021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90389-yhttps://doaj.org/toc/2045-2322Abstract High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical research and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground-truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.Laura K. YoungHannah E. SmithsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Laura K. Young Hannah E. Smithson Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images
description	Abstract High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical research and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground-truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.
format	article
author	Laura K. Young Hannah E. Smithson
author_facet	Laura K. Young Hannah E. Smithson
author_sort	Laura K. Young
title	Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images
title_short	Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images
title_full	Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images
title_fullStr	Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images
title_full_unstemmed	Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images
title_sort	emulated retinal image capture (erica) to test, train and validate processing of retinal images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8c9fa2db5ee640198e779001bce947a0
work_keys_str_mv	AT laurakyoung emulatedretinalimagecaptureericatotesttrainandvalidateprocessingofretinalimages AT hannahesmithson emulatedretinalimagecaptureericatotesttrainandvalidateprocessingofretinalimages
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Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images

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