  Purpose: Develop a deep learning-based automated method to segment meibomian
glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio,
estimate the meiboscore, and remove specular reflections from infrared images.
Methods: A total of 1600 meibography images were captured in a clinical
setting. 1000 images were precisely annotated with multiple revisions by
investigators and graded 6 times by meibomian gland dysfunction (MGD) experts.
Two deep learning (DL) models were trained separately to segment areas of the
MG and eyelid. Those segmentation were used to estimate MG ratio and
meiboscores using a classification-based DL model. A generative adversarial
network was implemented to remove specular reflections from original images.
Results: The mean ratio of MG calculated by investigator annotation and DL
segmentation was consistent 26.23% vs 25.12% in the upper eyelids and 32.34%
vs. 32.29% in the lower eyelids, respectively. Our DL model achieved 73.01%
accuracy for meiboscore classification on validation set and 59.17% accuracy
when tested on images from independent center, compared to 53.44% validation
accuracy by MGD experts. The DL-based approach successfully removes reflection
from the original MG images without affecting meiboscore grading. Conclusions:
DL with infrared meibography provides a fully automated, fast quantitative
evaluation of MG morphology (MG Segmentation, MG area, MG ratio, and
meiboscore) which are sufficiently accurate for diagnosing dry eye disease.
Also, the DL removes specular reflection from images to be used by
ophthalmologists for distraction-free assessment.
