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In this paper we present a new method for cystoid macular edema (CME)segmentation in retinal Optical Coherence Tomography (OCT) images, using afully convolutional neural network (FCN) and a fully connected conditionalrandom fields (dense CRFs). As a first step, the framework trains the FCN modelto extract features from retinal layers in OCT images, which exhibit CME, andthen segments CME regions using the trained model. Thereafter, dense CRFs areused to refine the segmentation according to the edema appearance. We havetrained and tested the framework with OCT images from 10 patients with diabeticmacular edema (DME). Our experimental results show that fluid and concretemacular edema areas were segmented with good adherence to boundaries. Asegmentation accuracy of $0.61\pm 0.21$ (Dice coefficient) was achieved, withrespect to the ground truth, which compares favourably with the previousstate-of-the-art that used a kernel regression based method ($0.51\pm 0.34$).Our approach is versatile and we believe it can be easily adapted to detectother macular defects.

Cystoid macular edema segmentation of Optical Coherence Tomography images using fully convolutional neural networks and fully connected CRFs