A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs

Abstract Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on Optical coherence tomography (OCT) technol...

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Autores principales:	Alireza Tavakkoli, Sharif Amit Kamran, Khondker Fariha Hossain, Stewart Lee Zuckerbrod
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ec6869771f0945e7be893d40e0077b27
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spelling	oai:doaj.org-article:ec6869771f0945e7be893d40e0077b272021-12-02T11:43:35ZA novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs10.1038/s41598-020-78696-22045-2322https://doaj.org/article/ec6869771f0945e7be893d40e0077b272020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78696-2https://doaj.org/toc/2045-2322Abstract Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on Optical coherence tomography (OCT) technology, called OCT angiography (OCTA), capable of visualizing retina vasculature. However, due to its cost and limited view, OCTA technology is not widely used. Retinal fundus photography is a safe imaging technique used for capturing the overall structure of the retina. In order to visualize retinal vasculature without the need for FA and in a cost-effective, non-invasive, and accurate manner, we propose a deep learning conditional generative adversarial network (GAN) capable of producing FA images from fundus photographs. The proposed GAN produces anatomically accurate angiograms, with similar fidelity to FA images, and significantly outperforms two other state-of-the-art generative algorithms ( $$p<.001$$ p < . 001 and $$p<.0001$$ p < . 0001 ). Furthermore, evaluations by experts shows that our proposed model produces such high quality FA images that are indistinguishable from real angiograms. Our model as the first application of artificial intelligence and deep learning to medical image translation, by employing a theoretical framework capable of establishing a shared feature-space between two domains (i.e. funduscopy and fluorescein angiography) provides an unrivaled way for the translation of images from one domain to the other.Alireza TavakkoliSharif Amit KamranKhondker Fariha HossainStewart Lee ZuckerbrodNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alireza Tavakkoli Sharif Amit Kamran Khondker Fariha Hossain Stewart Lee Zuckerbrod A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs
description	Abstract Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on Optical coherence tomography (OCT) technology, called OCT angiography (OCTA), capable of visualizing retina vasculature. However, due to its cost and limited view, OCTA technology is not widely used. Retinal fundus photography is a safe imaging technique used for capturing the overall structure of the retina. In order to visualize retinal vasculature without the need for FA and in a cost-effective, non-invasive, and accurate manner, we propose a deep learning conditional generative adversarial network (GAN) capable of producing FA images from fundus photographs. The proposed GAN produces anatomically accurate angiograms, with similar fidelity to FA images, and significantly outperforms two other state-of-the-art generative algorithms ( $$p<.001$$ p < . 001 and $$p<.0001$$ p < . 0001 ). Furthermore, evaluations by experts shows that our proposed model produces such high quality FA images that are indistinguishable from real angiograms. Our model as the first application of artificial intelligence and deep learning to medical image translation, by employing a theoretical framework capable of establishing a shared feature-space between two domains (i.e. funduscopy and fluorescein angiography) provides an unrivaled way for the translation of images from one domain to the other.
format	article
author	Alireza Tavakkoli Sharif Amit Kamran Khondker Fariha Hossain Stewart Lee Zuckerbrod
author_facet	Alireza Tavakkoli Sharif Amit Kamran Khondker Fariha Hossain Stewart Lee Zuckerbrod
author_sort	Alireza Tavakkoli
title	A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs
title_short	A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs
title_full	A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs
title_fullStr	A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs
title_full_unstemmed	A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs
title_sort	novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ec6869771f0945e7be893d40e0077b27
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A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs

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