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ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods

Laura K Young, Hannah E Smithson
doi: https://doi.org/10.1101/2021.02.22.432253
Laura K Young
1Biosciences Institute, Newcastle University, Newcastle, NE2 4HH, UK
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  • For correspondence: laura.k.young@newcastle.ac.uk
Hannah E Smithson
2Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, UK
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ABSTRACT

High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 23, 2021.
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ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods
Laura K Young, Hannah E Smithson
bioRxiv 2021.02.22.432253; doi: https://doi.org/10.1101/2021.02.22.432253
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ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods
Laura K Young, Hannah E Smithson
bioRxiv 2021.02.22.432253; doi: https://doi.org/10.1101/2021.02.22.432253

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