A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images

Purpose: To develop a deep learning approach to digitally segmenting meibomian gland atrophy area and computing percent atrophy in meibography images. Methods: A total of 706 meibography images with corresponding meiboscores were collected and annotated for each one with eyelid and atrophy regions. The dataset was then divided into the development and evaluation sets. The development set was used to train and tune the deep learning model, while the evaluation set was used to evaluate the performance of the model. Results: Four hundred ninety-seven meibography images were used for training and tuning the deep learning model while the remaining 209 images were used for evaluations. The algorithm achieves 95.6% meiboscore grading accuracy on average, largely outperforming the lead clinical investigator (LCI) by 16.0% and the clinical team by 40.6%. Our algorithm also achieves 97.6% and 95.4% accuracy for eyelid and atrophy segmentations, respectively, as well as 95.5% and 66.7% mean intersection over union accuracies (mean IU), respectively. The average root-mean-square deviation (RMSD) of the percent atrophy prediction is 6.7%. Conclusions: The proposed deep learning approach can automatically segment the total eyelid and meibomian gland atrophy regions, as well as compute percent atrophy with high accuracy and consistency. This provides quantitative information of the gland atrophy severity based on meibography images. Translational Relevance: Based on deep neural networks, the study presents an accurate and consistent gland atrophy evaluation method for meibography images, and may contribute to improved understanding of meibomian gland dysfunction.