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.
Highlights
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)
A reliable automatic MG segmentation technique may overcome the difficulties of manual image segmentation, as infrared meibography images often contain various artifacts such as low contrast, non-uniform illumination, defocus gland area, or specular reflections which make image segmentation more c hallenging[13]
In a previous study[18], we have compared intensity-thresholding, region growing, and deep learning method to automatic analysis of IR meibography images. Among these three implemented approaches, we have demonstrated that deep learning could produce high quality and reliable results for the challenging task of automated IR meibography image segmentation and quantification
Summary
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). Infrared (IR) Meibography is a well-established non-contact optical imaging technique, which uses IR illumination to depict MG morphology by examining the everted eyelid[7]. It is widely accepted, and recommended to image and quantify MG during dry-eye examination. Koh et al.[12], Llorens-Quintana et al.[13], Arita et al.[14], Celik et al.[15], and Koprowski et al.[16] have proposed automatic MG segmentation methods All of these methods rely on intensity-thresholding based image
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