Mapping the ocular surface from monocular videos with an application to dry eye disease grading

TL;DR

This paper introduces a novel monocular video-based method for 3D ocular surface tracking and severity grading of dry eye disease, outperforming existing techniques and enhancing diagnostic accuracy.

Contribution

It presents the first approach to DED diagnosis using monocular videos with unsupervised registration and severity grading, incorporating shape priors and joint CNN architectures.

Findings

Outperforms state-of-the-art registration with 0.48% error

Improves DED severity classification by 0.20 AUC

First monocular video-based DED diagnostic method

Abstract

With a prevalence of 5 to 50%, Dry Eye Disease (DED) is one of the leading reasons for ophthalmologist consultations. The diagnosis and quantification of DED usually rely on ocular surface analysis through slit-lamp examinations. However, evaluations are subjective and non-reproducible. To improve the diagnosis, we propose to 1) track the ocular surface in 3-D using video recordings acquired during examinations, and 2) grade the severity using registered frames. Our registration method uses unsupervised image-to-depth learning. These methods learn depth from lights and shadows and estimate pose based on depth maps. However, DED examinations undergo unresolved challenges including a moving light source, transparent ocular tissues, etc. To overcome these and estimate the ego-motion, we implement joint CNN architectures with multiple losses incorporating prior known information, namely the shape of the eye, through semantic segmentation as well as sphere fitting. The achieved tracking errors outperform the state-of-the-art, with a mean Euclidean distance as low as 0.48% of the image width on our test set. This registration improves the DED severity classification by a 0.20 AUC difference. The proposed approach is the first to address DED diagnosis with supervision from monocular videos