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

arXiv: https://arxiv.org/abs/1704.02161 · HTML: https://mlsanity.com/paper/1704.02161/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2017.

## Built on by (most cited first, 30 of 37)

- [Uncertainty guided semi-supervised segmentation of retinal layers in OCT images](https://mlsanity.com/paper/2103.02083/) (2021)
- [Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation](https://mlsanity.com/paper/2204.07613/) (2022)
- [SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors](https://mlsanity.com/paper/2207.00458/) (2022)
- [Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations](https://mlsanity.com/paper/2312.01726/) (2023)
- [MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis](https://mlsanity.com/paper/2506.08900/) (2025)
- [RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation](https://mlsanity.com/paper/2209.12468/) (2022)
- [Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/2208.14635/) (2022)
- [Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification](https://mlsanity.com/paper/2210.14799/) (2022)
- [Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images](https://mlsanity.com/paper/2203.02390/) (2022)
- [Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing](https://mlsanity.com/paper/2301.07204/) (2023)
- [Retinal Image Segmentation with Small Datasets](https://mlsanity.com/paper/2303.05110/) (2023)
- [Light-weight Retinal Layer Segmentation with Global Reasoning](https://mlsanity.com/paper/2404.16346/) (2024)
- [FourierNet: Shape-Preserving Network for Henle's Fiber Layer Segmentation in Optical Coherence Tomography Images](https://mlsanity.com/paper/2201.06435/) (2022)
- [A deep learning network with differentiable dynamic programming for retina OCT surface segmentation](https://mlsanity.com/paper/2210.06335/) (2022)
- [SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT](https://mlsanity.com/paper/2509.20864/) (2025)
- [Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty](https://mlsanity.com/paper/2102.07846/) (2021)
- [Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review](https://mlsanity.com/paper/2109.02322/) (2021)
- [nnUNet RASPP for Retinal OCT Fluid Detection, Segmentation and Generalisation over Variations of Data Sources](https://mlsanity.com/paper/2302.13195/) (2023)
- [Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation](https://mlsanity.com/paper/2410.01185/) (2024)
- [Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge](https://mlsanity.com/paper/2506.02976/) (2025)
- [Universal Wavelet Units in 3D Retinal Layer Segmentation](https://mlsanity.com/paper/2507.16119/) (2025)
- [Multi-Scale Global-Instance Prompt Tuning for Continual Test-time Adaptation in Medical Image Segmentation](https://mlsanity.com/paper/2602.05937/) (2026)
- [ReLaX: Retinal Layer Attribution for Guided Explanations of Automated Optical Coherence Tomography Classification](https://mlsanity.com/paper/2109.02436/) (2021)
- [A generalizable approach based on U-Net model for automatic Intra retinal cyst segmentation in SD-OCT images](https://mlsanity.com/paper/2202.00465/) (2022)
- [Superresolution and Segmentation of OCT scans using Multi-Stage adversarial Guided Attention Training](https://mlsanity.com/paper/2206.05277/) (2022)
- [GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans](https://mlsanity.com/paper/2206.13740/) (2022)
- [SwinVFTR: A Novel Volumetric Feature-learning Transformer for 3D OCT Fluid Segmentation](https://mlsanity.com/paper/2303.09233/) (2023)
- [Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations](https://mlsanity.com/paper/2403.15803/) (2024)
- [SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery](https://mlsanity.com/paper/2404.19481/) (2024)
- [Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Clinical Assessment of Diabetic Retinopathy Severity](https://mlsanity.com/paper/2503.01248/) (2025)

## Abstract (arXiv)

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.