Automated quantification of meibomian gland dropout in infrared meibography using deep learning
RK Saha, AMM Chowdhury, KS Na, GD Hwang… - The Ocular Surface, 2022 - Elsevier
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 …
(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 …
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