# Retinal Image Segmentation with Small Datasets

arXiv: https://arxiv.org/abs/2303.05110 · HTML: https://mlsanity.com/paper/2303.05110/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network](https://mlsanity.com/paper/1704.02161/) (2017)
- [Segmentation of retinal cysts from Optical Coherence Tomography volumes via selective enhancement](https://mlsanity.com/paper/1708.06197/) (2017)
- [Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network](https://mlsanity.com/paper/1710.04778/) (2017)

## Built on by (most cited first, 3 of 3)

- [Performance Analysis of UNet and Variants for Medical Image Segmentation](https://mlsanity.com/paper/2309.13013/) (2023)
- [Segmentation of Retinal Blood Vessels Using Deep Learning](https://mlsanity.com/paper/2303.09679/) (2023)
- [Skin Lesion Diagnosis Using Convolutional Neural Networks](https://mlsanity.com/paper/2305.11125/) (2023)

## Abstract (arXiv)

Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version. The Optical Coherence Tomography (OCT), a 3D scan of the retina with high qualitative information about the retinal morphology, can be used to diagnose and monitor changes in the retinal anatomy. Many Deep Learning (DL) methods have shared the success of developing an automated tool to monitor pathological changes in the retina. However, the success of these methods depend mainly on large datasets. To address the challenge from very small and limited datasets, we proposed a DL architecture termed CoNet (Coherent Network) for joint segmentation of layers and fluids in retinal OCT images on very small datasets (less than a hundred training samples). The proposed model was evaluated on the publicly available Duke DME dataset consisting of 110 B-Scans from 10 patients suffering from DME. Experimental results show that the proposed model outperformed both the human experts' annotation and the current state-of-the-art architectures by a clear margin with a mean Dice Score of 88% when trained on 55 images without any data augmentation.