ArticleScientific reports2021
Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- WRCANet: wavelet residual cross-attention network for meibomian gland segmentation.Biomedical optics express · 2026Article
- Molecular landscape connecting complications of dry eye disease: mechanisms, therapeutic targets, biomarkers, and future innovations.International ophthalmology · 2026Review
- ADAM-Net: Anatomy-Guided Attentive Unsupervised Domain Adaptation for Joint MG Segmentation and MGD Grading.Journal of imaging · 2026Article
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- AI driven quantitative analysis of meibomian glands in children and adolescents: a benchmark dataset study.Eye and vision (London, England) · 2025Article
- In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.Journal of imaging informatics in medicine · 2025Article
- Ophthalmic Segmentation and Analysis Software (OASIS): A Comprehensive Tool for Quantitative Evaluation of Meibography Images.Translational vision science & technology · 2025Article
- Morphological Characteristics of the Meibomian Glands: Impact of Demographics and Contact Lens Wear.Translational vision science & technology · 2025Article
- Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation.NPJ digital medicine · 2025Article
- A modular and adaptable approach for automated morphological feature extraction in meibography images.Scientific reports · 2025Article
- Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles.Diagnostics (Basel, Switzerland) · 2025Article
- The repeatability of tests for dry eye signs and symptoms in the dry eye assessment and management (DREAM) study.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2025Article
- Advances in AI-assisted quantification of dry eye indicators.Frontiers in medicine · 2025Review
- Internal validation of a convolutional neural network pipeline for assessing meibomian gland structure from meibography.Optometry and vision science : official publication of the American Academy of Optometry · 2025Article
- Quantitative evaluation of meibomian gland dysfunction via deep learning-based infrared image segmentation.Frontiers in artificial intelligence · 2025Article
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Red filter meibography by smartphones in patients with meibomian gland dysfunction: a validity and reliability study.BMJ open ophthalmology · 2024Article
- Role of artificial intelligence in cornea practice.Indian journal of ophthalmology · 2024Article
- Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm.Frontiers in cell and developmental biology · 2024Article
- Impact of Dyslipidemia on Tear Film and Meibomian Gland Dysfunction: A Cross-Sectional Study of the Interplay between Serum Lipid Profile and Ocular Surface Health.Journal of ophthalmology · 2024Article
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5 authors.
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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.
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