Deep learning based adaptive and automatic measurement of palpebral margin in eyelid morphology

Abstract

Accurate anatomical measurements of the eyelids are essential in periorbital plastic surgery for both disease treatment and procedural planning. Recent researches in eye diseases have adopted deep learning works to measure MRD. However, such works encounter challenges in practical implementation, and the model accuracy needs to be improved. Here, we have introduced a deep learning-based adaptive and automatic measurement (DeepAAM) by employing the U-Net architecture enhanced through attention mechanisms and multiple algorithms. DeepAAM enables adaptive image recognition and adjustment in practical application, and improves the measurement accuracy of Marginal Reflex Distance (MRD). Meanwhile, it for the first time measures the Margin Iris Intersectant Angle (MIA) as an innovative evaluation index. Besides, this fully automated method surpasses other models in terms of accuracy for iris and sclera segmentation. DeepAAM offers a novel, comprehensive, and objective approach to the quantification of ocular morphology.