  Optical coherence tomography (OCT) is used for non-invasive diagnosis of
diabetic macular edema assessing the retinal layers. In this paper, we propose
a new fully convolutional deep architecture, termed ReLayNet, for end-to-end
segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses
a contracting path of convolutional blocks (encoders) to learn a hierarchy of
contextual features, followed by an expansive path of convolutional blocks
(decoders) for semantic segmentation. ReLayNet is trained to optimize a joint
loss function comprising of weighted logistic regression and Dice overlap loss.
The framework is validated on a publicly available benchmark dataset with
comparisons against five state-of-the-art segmentation methods including two
deep learning based approaches to substantiate its effectiveness.
