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

本篇研究通过运用三维摄像来追踪眼表面，并利用 CNN 架构估算眼球运动，解决了眼表面分析在光源移动和透明眼组织等问题上的难点，从而提高了对干眼症的有效诊断。