Background/Aims Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma.
Method In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important measurements were created. 200 images from 58 patients (100 eyes) were used for testing.
Results With limited training data, the DCNN was able to detect the scleral spur on unseen anterior segment optical coherence tomography (ASOCT) images as accurately as an experienced ophthalmologist on the given test dataset and simultaneously isolated the AS structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT measurements and proposed an automated quality check process that asserts the reliability of these measurements. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. The total segmentation and measurement time for a single scan is less than 2 s.
Conclusion This is an essential step towards providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle-closure glaucoma.
- Anterior chamber
- Sclera and Episclera
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Contributors THP designed the experiments, algorithms and wrote the paper. SKD assisted in algorithm design. AA and VK assisted in data labelling and experiment design. SKD, AA, Z-DS, AHT and CB edited the manuscript. C-YC, MJAG and VK supervised the study and edited the manuscript.
Funding This work was supported by Singapore Ministry of Education Academic Research Funds Tier 1 (R-397-000-294-114 [MJAG]); Singapore Ministry of Education Tier 2 (R-397-000-280-112, R-397-000-308-112 [MJAG]); National Medical Research Council (NMRC/1442/2016, NMRC/CSA-SI/0012/2017 [C-YC]); National Medical Research Council (NMRC/CNIG/1135/2015 [VK]).
Competing interests The preprint version of this manuscript can be found at arXiv:1909.00331. MJAG and AHT are co-founders of Abyss Processing.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement No data are available.
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