Aim To develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2).
Methods The dataset consisted of 99 eyes from 67 participants enrolled in an international, multicentre, phase 2 MacTel2 clinical trial (NCT01949324). Each eye was imaged with spectral-domain OCT at three time points over 2 years. Retinal cavitations were manually segmented by a trained Reader and the retinal cavitation volume was calculated. Two convolutional neural networks (CNNs) were developed that operated in sequential stages. In the first stage, CNN1 classified whether a B-scan contained any retinal cavitations. In the second stage, CNN2 segmented the retinal cavitations in a B-scan. We evaluated the performance of the proposed method against alternative methods using several performance metrics and manual segmentations as the gold standard.
Results The proposed method was computationally efficient and accurately classified and segmented retinal cavitations on OCT images, with a sensitivity of 0.94, specificity of 0.80 and average Dice similarity coefficient of 0.94±0.07 across all time points. The proposed method produced measurements that were highly correlated with the manual measurements of retinal cavitation volume and change in retinal cavitation volume over time.
Conclusion The proposed method will be useful to help clinicians quantify retinal cavitations, assess changes over time and further investigate the clinical significance of these early structural changes observed in MacTel2.
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Contributors JL and SF designed the study. EC, MF and GJ provided the dataset used in the study. JL, CXC, JC, GJ and SF analysed and interpreted the data. JL drafted the manuscript. CXC, JC, EC, MF, GJ and SF proofed and revised the manuscript.
Funding Funding was provided in part by The Lowy Medical Research Institute, National Institutes of Health (P30 EY005722), and Unrestricted Grant from Research to Prevent Blindness.
Disclaimer The funding organizations had no role in the design or conduct of this research.
Competing interests Glenn J Jaffe (Consultant, Neurotech).
Patient consent for publication Not required.
Ethics approval This study adhered to the tenets of the Declaration of Helsinki, complied with the Health Insurance Portability and Accountability Act and Clinical Trials (USA and Australia) guidelines, and was approved by the institutional ethics committees at each participating centre.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement No data are available. N/A.
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