Tracking the Dynamics of the Tear Film Lipid Layer

Tejasvi Kothapalli, Charlie Shou, Jennifer Ding, Peter Wang, Tatyana Svitova, Andrew D. Graham, Stella Yu, Meng Lin

University of California, Berkeley

36th Conference on Neural Information Processing Systems (NeurIPS 2022)

Abstract

Dry Eye Disease (DED) is one of the most common ocular diseases: over five percent of US adults suffer from DED [4]. 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.

Blink Detection

Video Stabilization

Iris Segmentation

Lipid Layer Visual Enhancement

Optical Flow

Resources

Citation

                @inproceedings{
                    kothapalli_lipidlayer, 
                    title={Tracking the Dynamics of the Tear Film Lipid Layer}, 
                    author={Tejasvi Kothapalli and Charlie Shou 
                        and Jennifer Ding and Peter Wang 
                        and Tatyana Svitova and Andrew D. Graham 
                        and Stella Yu and Meng Lin}, 
                    year={2022}, 
                    booktitle={NeurIPS} 
                }
            

Acknowlegements

This work was supported by R21EY033881 (Lin/Yu), UCB-CRC Research Fund 51194 (Lin) and Roberta Smith Research Fund 13681 (Lin).