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.
@article{arxiv.1704.02161,
title = {ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network},
author = {Abhijit Guha Roy and Sailesh Conjeti and Sri Phani Krishna Karri and Debdoot Sheet and Amin Katouzian and Christian Wachinger and Nassir Navab},
journal= {arXiv preprint arXiv:1704.02161},
year = {2017}
}
Comments
Accepted for Publication at Biomedical Optics Express