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Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images
2018
Translational Vision Science & Technology
Purpose: To automatically and accurately segment geographic atrophy (GA) in spectral-domain optical coherence tomography (SD-OCT) images by constructing a voting system with deep neural networks without the use of retinal layer segmentation. Methods: An automatic GA segmentation method for SD-OCT images based on the deep network was constructed. The structure of the deep network was composed of five layers, including one input layer, three hidden layers, and one output layer. During the
doi:10.1167/tvst.7.1.1
pmid:29302382
pmcid:PMC5749649
fatcat:h3zc6n4v7rhfxdrdhlktmun3de