1. Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2

    Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2

    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 ...

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