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Purpose: To develop a deep learning approach to digitally-stain opticalcoherence tomography (OCT) images of the optic nerve head (ONH).  Methods: A horizontal B-scan was acquired through the center of the ONH usingOCT (Spectralis) for 1 eye of each of 100 subjects (40 normal & 60 glaucoma).All images were enhanced using adaptive compensation. A custom deep learningnetwork was then designed and trained with the compensated images to digitallystain (i.e. highlight) 6 tissue layers of the ONH. The accuracy of ouralgorithm was assessed (against manual segmentations) using the Dicecoefficient, sensitivity, and specificity. We further studied how compensationand the number of training images affected the performance of our algorithm.  Results: For images it had not yet assessed, our algorithm was able todigitally stain the retinal nerve fiber layer + prelamina, the retinal pigmentepithelium, all other retinal layers, the choroid, and the peripapillary scleraand lamina cribrosa. For all tissues, the mean dice coefficient was $0.84 \pm0.03$, the mean sensitivity $0.92 \pm 0.03$, and the mean specificity $0.99 \pm0.00$. Our algorithm performed significantly better when compensated imageswere used for training. Increasing the number of images (from 10 to 40) totrain our algorithm did not significantly improve performance, except for theRPE.  Conclusion. Our deep learning algorithm can simultaneously stain neural andconnective tissues in ONH images. Our approach offers a framework toautomatically measure multiple key structural parameters of the ONH that may becritical to improve glaucoma management.

A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head