Tracking the Dynamics of the Tear Film Lipid Layer

TL;DR

This paper introduces a computer vision-based method to analyze the tear film lipid layer's dynamics, aiding in the diagnosis of Dry Eye Disease by tracking its spread in videos.

Contribution

It presents a novel tracking algorithm for the tear film lipid layer using computer vision, with the first dataset of videos collected for this purpose.

Findings

Effective tracking of tear film lipid layer spread demonstrated

Method available at https://easytear-dev.github.io/

Potential to improve Dry Eye Disease diagnosis

Abstract

Dry Eye Disease (DED) is one of the most common ocular diseases: over five percent of US adults suffer from DED. Tear film instability is a known factor for DED, and is thought to be regulated in large part by the thin lipid layer that covers and stabilizes the tear film. In order to aid eye related disease diagnosis, this work proposes a novel paradigm in using computer vision techniques to numerically analyze the tear film lipid layer (TFLL) spread. Eleven videos of the tear film lipid layer spread are collected with a micro-interferometer and a subset are annotated. A tracking algorithm relying on various pillar computer vision techniques is developed. Our method can be found at https://easytear-dev.github.io/.