Significance: Quantifying Meibomian gland morphology from meibography images is used for diagnosis, treatment, and management of Meibomian gland dysfunction in clinics. A novel and automated method is described for quantifying Meibomian gland morphology from meibography images.
Purpose: Meibomian gland morphological abnormality is a common clinical sign of Meibomian gland dysfunction, yet there exist no automated methods that provide standard quantifications of morphological features for individual glands. This study introduces an automated artificial intelligence approach to segmenting individual Meibomian gland regions in infrared meibography images and analyzing their morphological features.
Methods: A total of 1443 meibography images were collected and annotated. The dataset was then divided into development and evaluation sets. The development set was used to train and tune deep learning models for segmenting glands and identifying ghost glands from images, while the evaluation set was used to evaluate the performance of the model. The gland segmentations were further used to analyze individual gland features, including gland local contrast, length, width and tortuosity.
Results: 1039 meibography images (including 486 upper and 553 lower eyelids) were used for training and tuning the deep learning model while the remaining 404 images (including 203 upper and 201 lower eyelids) were used for evaluations. The algorithm on average achieved 63\% mean intersection over union in segmenting glands, and 84.4\% sensitivity and 71.7\% specificity in identifying ghost glands. Morphological features of each gland were also fed to a support vector machine for analyzing their associations to ghost glands. Analysis of model coefficients indicated that low gland local contrast was the primary indicator for ghost glands.
Conclusions: The proposed approach can automatically segment individual Meibomian glands in infrared meibography images, identify ghost glands, and quantitatively analyze gland morphological features.