Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography

Abstract Meibomian glands (MG) are large sebaceous glands located below the tarsal conjunctiva and the abnormalities of these glands cause Meibomian gland dysfunction (MGD) which is responsible for evaporative dry eye disease (DED). Accurate MG segmentation is a key prerequisite for automated imagin...

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Autores principales:	Md Asif Khan Setu, Jens Horstmann, Stefan Schmidt, Michael E. Stern, Philipp Steven
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/81521c54131d43678c2babeb7875e399
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spelling	oai:doaj.org-article:81521c54131d43678c2babeb7875e3992021-12-02T14:26:07ZDeep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography10.1038/s41598-021-87314-82045-2322https://doaj.org/article/81521c54131d43678c2babeb7875e3992021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87314-8https://doaj.org/toc/2045-2322Abstract Meibomian glands (MG) are large sebaceous glands located below the tarsal conjunctiva and the abnormalities of these glands cause Meibomian gland dysfunction (MGD) which is responsible for evaporative dry eye disease (DED). Accurate MG segmentation is a key prerequisite for automated imaging based MGD related DED diagnosis. However, Automatic MG segmentation in infrared meibography is a challenging task due to image artifacts. A deep learning-based MG segmentation has been proposed which directly learns MG features from the training image dataset without any image pre-processing. The model is trained and evaluated using 728 anonymized clinical meibography images. Additionally, automatic MG morphometric parameters, gland number, length, width, and tortuosity assessment were proposed. The average precision, recall, and F1 score were achieved 83%, 81%, and 84% respectively on the testing dataset with AUC value of 0.96 based on ROC curve and dice coefficient of 84%. Single image segmentation and morphometric parameter evaluation took on average 1.33 s. To the best of our knowledge, this is the first time that a validated deep learning-based approach is applied in MG segmentation and evaluation for both upper and lower eyelids.Md Asif Khan SetuJens HorstmannStefan SchmidtMichael E. SternPhilipp StevenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Md Asif Khan Setu Jens Horstmann Stefan Schmidt Michael E. Stern Philipp Steven Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography
description	Abstract Meibomian glands (MG) are large sebaceous glands located below the tarsal conjunctiva and the abnormalities of these glands cause Meibomian gland dysfunction (MGD) which is responsible for evaporative dry eye disease (DED). Accurate MG segmentation is a key prerequisite for automated imaging based MGD related DED diagnosis. However, Automatic MG segmentation in infrared meibography is a challenging task due to image artifacts. A deep learning-based MG segmentation has been proposed which directly learns MG features from the training image dataset without any image pre-processing. The model is trained and evaluated using 728 anonymized clinical meibography images. Additionally, automatic MG morphometric parameters, gland number, length, width, and tortuosity assessment were proposed. The average precision, recall, and F1 score were achieved 83%, 81%, and 84% respectively on the testing dataset with AUC value of 0.96 based on ROC curve and dice coefficient of 84%. Single image segmentation and morphometric parameter evaluation took on average 1.33 s. To the best of our knowledge, this is the first time that a validated deep learning-based approach is applied in MG segmentation and evaluation for both upper and lower eyelids.
format	article
author	Md Asif Khan Setu Jens Horstmann Stefan Schmidt Michael E. Stern Philipp Steven
author_facet	Md Asif Khan Setu Jens Horstmann Stefan Schmidt Michael E. Stern Philipp Steven
author_sort	Md Asif Khan Setu
title	Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography
title_short	Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography
title_full	Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography
title_fullStr	Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography
title_full_unstemmed	Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography
title_sort	deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/81521c54131d43678c2babeb7875e399
work_keys_str_mv	AT mdasifkhansetu deeplearningbasedautomaticmeibomianglandsegmentationandmorphologyassessmentininfraredmeibography AT jenshorstmann deeplearningbasedautomaticmeibomianglandsegmentationandmorphologyassessmentininfraredmeibography AT stefanschmidt deeplearningbasedautomaticmeibomianglandsegmentationandmorphologyassessmentininfraredmeibography AT michaelestern deeplearningbasedautomaticmeibomianglandsegmentationandmorphologyassessmentininfraredmeibography AT philippsteven deeplearningbasedautomaticmeibomianglandsegmentationandmorphologyassessmentininfraredmeibography
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Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography

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