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Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning
2018
Biomedical Optics Express
Automated measurements of the human cone mosaic requires the identification of individual cone photoreceptors. The current gold standard, manual labeling, is a tedious process and can not be done in a clinically useful timeframe. As such, we present an automated algorithm for identifying cone photoreceptors in adaptive optics optical coherence tomography (AO-OCT) images. Our approach fine-tunes a pre-trained convolutional neural network originally trained on AO scanning laser ophthalmoscope
doi:10.1364/boe.9.005353
pmid:30460133
pmcid:PMC6238943
fatcat:olqo5ihhpjfgrbwkej2jrpmb6y