Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks

Abstract Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated a...

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Autores principales:	David Cunefare, Leyuan Fang, Robert F. Cooper, Alfredo Dubra, Joseph Carroll, Sina Farsiu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e9741e0bd1954ed1a093b2300dddbf58
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spelling	oai:doaj.org-article:e9741e0bd1954ed1a093b2300dddbf582021-12-02T15:06:24ZOpen source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks10.1038/s41598-017-07103-02045-2322https://doaj.org/article/e9741e0bd1954ed1a093b2300dddbf582017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07103-0https://doaj.org/toc/2045-2322Abstract Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online.David CunefareLeyuan FangRobert F. CooperAlfredo DubraJoseph CarrollSina FarsiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Cunefare Leyuan Fang Robert F. Cooper Alfredo Dubra Joseph Carroll Sina Farsiu Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks
description	Abstract Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online.
format	article
author	David Cunefare Leyuan Fang Robert F. Cooper Alfredo Dubra Joseph Carroll Sina Farsiu
author_facet	David Cunefare Leyuan Fang Robert F. Cooper Alfredo Dubra Joseph Carroll Sina Farsiu
author_sort	David Cunefare
title	Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks
title_short	Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks
title_full	Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks
title_fullStr	Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks
title_full_unstemmed	Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks
title_sort	open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e9741e0bd1954ed1a093b2300dddbf58
work_keys_str_mv	AT davidcunefare opensourcesoftwareforautomaticdetectionofconephotoreceptorsinadaptiveopticsophthalmoscopyusingconvolutionalneuralnetworks AT leyuanfang opensourcesoftwareforautomaticdetectionofconephotoreceptorsinadaptiveopticsophthalmoscopyusingconvolutionalneuralnetworks AT robertfcooper opensourcesoftwareforautomaticdetectionofconephotoreceptorsinadaptiveopticsophthalmoscopyusingconvolutionalneuralnetworks AT alfredodubra opensourcesoftwareforautomaticdetectionofconephotoreceptorsinadaptiveopticsophthalmoscopyusingconvolutionalneuralnetworks AT josephcarroll opensourcesoftwareforautomaticdetectionofconephotoreceptorsinadaptiveopticsophthalmoscopyusingconvolutionalneuralnetworks AT sinafarsiu opensourcesoftwareforautomaticdetectionofconephotoreceptorsinadaptiveopticsophthalmoscopyusingconvolutionalneuralnetworks
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Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks

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