Deep Learning-based Quantification of Anterior Segment OCT Parameters

Deep Learning-based Quantification of Anterior Segment OCT Parameters

Ophthalmology Science

10.1016/j.xops.2023.100360

https://doi.org/10.1016/j.xops.2023.100360

Zhi Da Soh, Mingrui Tan, Monisha Esther Nongpiur, Marco Yu, Chaoxu Qian, Yih Chung Tham, Victor Koh, Tin Aung, Xinxing Xu, Yong Liu, Ching-Yu Cheng

04 July 2023

Soh, Z. D., Tan, M., Nongpiur, M. E., Yu, M., Qian, C., Tham, Y. C., Koh, V., Aung, T., Xu, X., Liu, Y., & Cheng, C.-Y. (2024). Deep Learning-based Quantification of Anterior Segment OCT Parameters. Ophthalmology Science, 4(1), 100360. https://doi.org/10.1016/j.xops.2023.100360

Objective To develop and validate a deep learning algorithm that could automate the annotation of scleral spur (SS) and segmentation of anterior chamber (AC) structures for measurements of AC, iris, and angle width parameters in anterior segment OCT (ASOCT) scans. Design Cross-sectional study. Subjects Data from 2 population-based studies (i.e., the Singapore Chinese Eye Study and Singapore Malay Eye Study) and 1 clinical study on angle-closure disease were included in algorithm development. A separate clinical study on angle-closure disease was used for external validation. Method Image contrast of ASOCT scans were first enhanced with CycleGAN. We utilized a heat map regression approach with coarse-to-fine framework for SS annotation. Then, an ensemble network of U-Net, full resolution residual network, and full resolution U-Net was used for structure segmentation. Measurements obtained from predicted SSs and structure segmentation were measured and compared with measurements obtained from manual SS annotation and structure segmentation (i.e., ground truth). Main Outcome Measures We measured Euclidean distance and intraclass correlation coefficients (ICC) to evaluate SS annotation and Dice similarity coefficient for structure segmentation. The ICC, Bland–Altman plot, and repeatability coefficient were used to evaluate agreement and precision of measurements. Results For SS annotation, our algorithm achieved a Euclidean distance of 124.7 μm, ICC ≥ 0.95, and a 3.3% error rate. For structure segmentation, we obtained Dice similarity coefficient ≥ 0.91 for cornea, iris, and AC segmentation. For angle width measurements, ≥ 95% of data points were within the 95% limits-of-agreement in Bland–Altman plot with insignificant systematic bias (all P > 0.12). The ICC ranged from 0.71–0.87 for angle width measurements, 0.54 for IT750, 0.83–0.85 for other iris measurements, and 0.89–0.99 for AC measurements. Using the same SS coordinates from a human expert, measurements obtained from our algorithm were generally less variable than measurements obtained from a semiautomated angle assessment program. Conclusion We developed a deep learning algorithm that could automate SS annotation and structure segmentation in ASOCT scans like human experts, in both open-angle and angle-closure eyes. This algorithm reduces the time needed and subjectivity in obtaining ASOCT measurements.

Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

This research / project is supported by the Agency for Science, Technology and Research - Advanced Manufacturing and Engineering Programmatic Grant
Grant Reference no. : A20H4b0141

This research / project is supported by the National Medical Research Council - Clinician Scientist Individual Research Grant
Grant Reference no. : NMRC/CIRG/1442/2016 and NMRC/CIRG/1488/2018

2024 Published by Elsevier Inc. on behalf of the American Academy of Ophthalmology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

https://oar.a-star.edu.sg/communities-collections/articles/22130

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