# ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network

Source: [ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network](<https://www.sota2.com/research/paper/68fa637f1e209131ce9fee7a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:42:16.909Z

Authors: Abhijit Guha Roy, Sailesh Conjeti, Sri Phani Krishna Karri, Debdoot Sheet, Amin Katouzian, Christian Wachinger, Nassir Navab

Published: 2017-04-07T09:50:05.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Retinal layer boundary segmentation on Clinical OCT dataset (test)](<https://www.sota2.com/research/sota/retinal-layer-boundary-segmentation-on-clinical-oct-dataset-test.md>): Retinal layer boundary segmentation; Clinical OCT dataset (test); RMSE (ILM boundary): 3.6.

- [Layer Segmentation on Retinal OCT (test)](<https://www.sota2.com/research/sota/layer-segmentation-on-retinal-oct-test.md>): Layer Segmentation; Retinal OCT (test); RMSE (µm): 2.69.

- [Retinal Layer Segmentation on DUKE DME](<https://www.sota2.com/research/sota/retinal-layer-segmentation-on-duke-dme.md>): Retinal Layer Segmentation; DUKE DME; ILM Boundary Error: 3.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1704.02161>)

[PDF](<https://arxiv.org/pdf/1704.02161.pdf>)