1. Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration

    Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration

    Purpose To evaluate an automated segmentation algorithm with a convolutional neural network (CNN) to quantify and detect intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), and subretinal hyperreflective material (SHRM) through analyses of spectral domain optical coherence tomography (SD-OCT) images from patients with neovascular age-related macular degeneration (nAMD). Design Reliability and validity analysis of a diagnostic tool. Methods We constructed a dataset including 930 B-scans from 93 eyes of 93 patients with nAMD. A CNN-based deep neural network was trained using 11550 augmented images derived from 550 B-scans. The performance of the trained network was evaluated using a ...

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