A novel retinal ganglion cell quantification tool based on deep learning

Abstract Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help...

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Autores principales:	Luca Masin, Marie Claes, Steven Bergmans, Lien Cools, Lien Andries, Benjamin M. Davis, Lieve Moons, Lies De Groef
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/25be3582945645a6b4595e350848aeb5
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spelling	oai:doaj.org-article:25be3582945645a6b4595e350848aeb52021-12-02T15:23:08ZA novel retinal ganglion cell quantification tool based on deep learning10.1038/s41598-020-80308-y2045-2322https://doaj.org/article/25be3582945645a6b4595e350848aeb52021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80308-yhttps://doaj.org/toc/2045-2322Abstract Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.Luca MasinMarie ClaesSteven BergmansLien CoolsLien AndriesBenjamin M. DavisLieve MoonsLies De GroefNature 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 Luca Masin Marie Claes Steven Bergmans Lien Cools Lien Andries Benjamin M. Davis Lieve Moons Lies De Groef A novel retinal ganglion cell quantification tool based on deep learning
description	Abstract Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.
format	article
author	Luca Masin Marie Claes Steven Bergmans Lien Cools Lien Andries Benjamin M. Davis Lieve Moons Lies De Groef
author_facet	Luca Masin Marie Claes Steven Bergmans Lien Cools Lien Andries Benjamin M. Davis Lieve Moons Lies De Groef
author_sort	Luca Masin
title	A novel retinal ganglion cell quantification tool based on deep learning
title_short	A novel retinal ganglion cell quantification tool based on deep learning
title_full	A novel retinal ganglion cell quantification tool based on deep learning
title_fullStr	A novel retinal ganglion cell quantification tool based on deep learning
title_full_unstemmed	A novel retinal ganglion cell quantification tool based on deep learning
title_sort	novel retinal ganglion cell quantification tool based on deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/25be3582945645a6b4595e350848aeb5
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A novel retinal ganglion cell quantification tool based on deep learning

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