Automatic assessment of tear film and tear meniscus parameters in healthy subjects using ultrahigh-resolution optical coherence tomography

Hannes Stegmann; Valentin Aranha Dos Santos; Alina Messner; Angelika Unterhuber; Doreen Schmidl; Gerhard Garhöfer; Leopold Schmetterer; René Marcel Werkmeister

doi:10.1364/BOE.10.002744

Automatic assessment of tear film and tear meniscus parameters in healthy subjects using ultrahigh-resolution optical coherence tomography

Biomed Opt Express. 2019 May 9;10(6):2744-2756. doi: 10.1364/BOE.10.002744. eCollection 2019 Jun 1.

Authors

Hannes Stegmann^{1

2}, Valentin Aranha Dos Santos^{1

2}, Alina Messner¹, Angelika Unterhuber¹, Doreen Schmidl³, Gerhard Garhöfer³, Leopold Schmetterer^{1

2

3

4

5

6}, René Marcel Werkmeister^{1

2}

Affiliations

¹ Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria.
² Christian Doppler Laboratory for Ocular and Dermal Effects of Thiomers, Medical University of Vienna, Austria.
³ Department of Clinical Pharmacology, Medical University of Vienna, Austria.
⁴ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
⁵ Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
⁶ Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore.

Abstract

Many different parameters exist for the investigation of tear film dynamics. We present a new tear meniscus segmentation algorithm which automatically extracts tear meniscus area (TMA), height (TMH), depth (TMD) and radius (TMR) from UHR-OCT measurements and apply it to a data set including repeated measurements from ten healthy subjects. Mean values and standard deviations are 0.0174 ± 0.007 mm², 0.272 ± 0.069 mm, 0.191 ± 0.049 mm and 0.309 ± 0.123 mm for TMA, TMH, TMD and TMR, respectively. A significant correlation was found between all respective tear meniscus parameter pairs (all p < 0.001, all Pearson's r ≥ 0.657). Challenges, limitations and potential improvements related to the data acquisition and the algorithm itself are discussed. The automatic segmentation of tear meniscus measurements acquired with UHR-OCT might help in a clinical setting to further understand the tear film and related medical conditions like dry eye disease.