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

• Main Authors: Stegmann, Hannes, Aranha dos Santos, Valentin, Messner, Alina, Unterhuber, Angelika, Schmidl, Doreen, Garhöfer, Gerhard, Schmetterer, Leopold, Werkmeister, René Marcel
• 其他作者: Lee Kong Chian School of Medicine (LKCMedicine)
• 格式: Article
• 語言: English
• 出版: 2019
• 主題: Tear Film; Optical Coherence Tomography; Science::Medicine
• 在線閱讀: https://hdl.handle.net/10356/86179; http://hdl.handle.net/10220/49857
• 機構: Nanyang Technological University

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.