Macular hole morphology and measurement using an automated three dimensional image segmentation algorithm

Chen, Yunzi; Nasrulloh, Amar; Wilson, Ian; Caspar, Geenen; Maged, Habib; Obara, Boguslaw; Steel, David

Abstract

Objective: Full-thickness macular holes (MH) are classified principally by size, which is one of the strongest predictors of anatomical and visual success. Using a three-dimensional (3D) automated image processing algorithm, we analysed optical coherence tomography (OCT) images of 104 MH of patients, comparing MH dimensions and morphology with clinician-acquired two-dimensional measurements. Methods and Analysis: All patients underwent a high-density central horizontal scanning OCT protocol. Two independent clinicians measured the minimum linear diameter (MLD) and maximum base diameter. OCT images were also analysed using an automated 3D segmentation algorithm which produced key parameters including the respective maximum and minimum diameter of the minimum area (MA) of the MH, as well as volume and surface area. Results: Using the algorithm-derived values, MH were found to have significant asymmetry in all dimensions. The minima of the MA were typically approximately 90° to the horizontal, and differed from their maxima by 55 μm. The minima of the MA differed from the human-measured MLD by a mean of nearly 50 μm, with significant interobserver variability. The resultant differences led to reclassification using the International Vitreomacular Traction Study Group classification in a quarter of the patients (p=0.07). Conclusion: MH are complex shapes with significant asymmetry in all dimensions. We have shown how 3D automated analysis of MH describes their dimensions more accurately and repeatably than human assessment. This could be used in future studies investigating hole progression and outcome to help guide optimum treatments.

Citation

Chen, Y., Nasrulloh, A., Wilson, I., Caspar, G., Maged, H., Obara, B., & Steel, D. (2020). Macular hole morphology and measurement using an automated three dimensional image segmentation algorithm. BMJ Open Ophthalmology, 5(1), Article e000404. https://doi.org/10.1136/bmjophth-2019-000404

• Journal Article Type: Article
• Acceptance Date: Feb 13, 2020
• Online Publication Date: Aug 16, 2020
• Publication Date: Aug 16, 2020
• Deposit Date: Feb 13, 2020
• Publicly Available Date: Aug 20, 2020
• Journal: BMJ Open Ophthalmology
• Electronic ISSN: 2397-3269
• Publisher: BMJ Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 1
• Article Number: e000404
• DOI: https://doi.org/10.1136/bmjophth-2019-000404
• Public URL: https://durham-repository.worktribe.com/output/1277390