Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning

The accurate detection of corneal edema has become a topic of growing interest with the generalization of endothelial keratoplasty. Despite recent advances in deep learning for corneal edema detection, the problem of minimal edema remains challenging. Using transfer learning and a limited training s...

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Autores principales:	Pierre Zéboulon, Wassim Ghazal, Karen Bitton, Damien Gatinel
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning corneal edema Fuchs endothelial corneal dystrophy optical coherence tomography Applied optics. Photonics TA1501-1820
Acceso en línea:	https://doaj.org/article/a8a1cbadf0da496da69e383adacd2d0e
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spelling	oai:doaj.org-article:a8a1cbadf0da496da69e383adacd2d0e2021-11-25T18:43:13ZSeparate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning10.3390/photonics81104832304-6732https://doaj.org/article/a8a1cbadf0da496da69e383adacd2d0e2021-10-01T00:00:00Zhttps://www.mdpi.com/2304-6732/8/11/483https://doaj.org/toc/2304-6732The accurate detection of corneal edema has become a topic of growing interest with the generalization of endothelial keratoplasty. Despite recent advances in deep learning for corneal edema detection, the problem of minimal edema remains challenging. Using transfer learning and a limited training set of 11 images, we built a model to segment the corneal epithelium, which is part of a three-model pipeline to detect corneal edema. A second and a third model are used to detect edema on the stroma alone and on the epithelium. A validation set of 233 images from 30 patients consisting of three groups (Normal, Minimal Edema and important Edema) was used to compare the results of our new pipeline to our previous model. The mean edema fraction (EF), defined as the number of pixels detected as edema divided by the total number of pixels of the cornea, was calculated for each image. With our previous model, the mean EF was not statistically different between the Normal and Minimal Edema groups (<i>p</i> = 0.24). With the current pipeline, the mean EF was higher in the Minimal Edema group compared to the Normal group (<i>p</i> < 0.01). The described pipeline constitutes an adjustable framework for the detection of corneal edema based on optical coherence tomography and yields better performances in cases of minimal or localized edema.Pierre ZéboulonWassim GhazalKaren BittonDamien GatinelMDPI AGarticledeep learningcorneal edemaFuchs endothelial corneal dystrophyoptical coherence tomographyApplied optics. PhotonicsTA1501-1820ENPhotonics, Vol 8, Iss 483, p 483 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning corneal edema Fuchs endothelial corneal dystrophy optical coherence tomography Applied optics. Photonics TA1501-1820
spellingShingle	deep learning corneal edema Fuchs endothelial corneal dystrophy optical coherence tomography Applied optics. Photonics TA1501-1820 Pierre Zéboulon Wassim Ghazal Karen Bitton Damien Gatinel Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning
description	The accurate detection of corneal edema has become a topic of growing interest with the generalization of endothelial keratoplasty. Despite recent advances in deep learning for corneal edema detection, the problem of minimal edema remains challenging. Using transfer learning and a limited training set of 11 images, we built a model to segment the corneal epithelium, which is part of a three-model pipeline to detect corneal edema. A second and a third model are used to detect edema on the stroma alone and on the epithelium. A validation set of 233 images from 30 patients consisting of three groups (Normal, Minimal Edema and important Edema) was used to compare the results of our new pipeline to our previous model. The mean edema fraction (EF), defined as the number of pixels detected as edema divided by the total number of pixels of the cornea, was calculated for each image. With our previous model, the mean EF was not statistically different between the Normal and Minimal Edema groups (<i>p</i> = 0.24). With the current pipeline, the mean EF was higher in the Minimal Edema group compared to the Normal group (<i>p</i> < 0.01). The described pipeline constitutes an adjustable framework for the detection of corneal edema based on optical coherence tomography and yields better performances in cases of minimal or localized edema.
format	article
author	Pierre Zéboulon Wassim Ghazal Karen Bitton Damien Gatinel
author_facet	Pierre Zéboulon Wassim Ghazal Karen Bitton Damien Gatinel
author_sort	Pierre Zéboulon
title	Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning
title_short	Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning
title_full	Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning
title_fullStr	Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning
title_full_unstemmed	Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning
title_sort	separate detection of stromal and epithelial corneal edema on optical coherence tomography using a deep learning pipeline and transfer learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a8a1cbadf0da496da69e383adacd2d0e
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Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning

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