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Segmentation of retinal layers from Optical Coherence Tomography (OCT)volumes is a fundamental problem for any computer aided diagnostic algorithmdevelopment. This requires preprocessing steps such as denoising, region ofinterest extraction, flattening and edge detection all of which involveseparate parameter tuning. In this paper, we explore deep learning techniquesto automate all these steps and handle the presence/absence of pathologies. Amodel is proposed consisting of a combination of Convolutional Neural Network(CNN) and Long Short Term Memory (LSTM). The CNN is used to extract layers ofinterest image and extract the edges, while the LSTM is used to trace the layerboundary. This model is trained on a mixture of normal and AMD cases usingminimal data. Validation results on three public datasets show that thepixel-wise mean absolute error obtained with our system is 1.30 plus or minus0.48 which is lower than the inter-marker error of 1.79 plus or minus 0.76. Ourmodel's performance is also on par with the existing methods.

A deep learning framework for segmentation of retinal layers from OCT images