Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2. Issue 3 (23rd November 2020)

• Record Type: Journal Article
• Authors: Loo, Jessica; Cai, Cindy X; Choong, John; Chew, Emily Y; Friedlander, Martin; Jaffe, Glenn J; Farsiu, Sina
• Is Part Of: British journal of ophthalmology. Volume 106:Issue 3(2022)
• Page Start: 396
• Page End: 402
• Publication Date: 2020-11-23
• Subjects: imaging -- retina; Ophthalmology -- Periodicals; 617.7
• DOI: 10.1136/bjophthalmol-2020-317131
• Languages: English
• ISSNs: 0007-1161

Abstract

Abstract : 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.