# Retinal vessel segmentation based on Fully Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1812.07110 · HTML: https://mlsanity.com/paper/1812.07110/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

## Built on by (most cited first, 25 of 25)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation](https://mlsanity.com/paper/2210.08168/) (2022)
- [RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2112.11078/) (2021)
- [Local Intensity Order Transformation for Robust Curvilinear Object Segmentation](https://mlsanity.com/paper/2202.12587/) (2022)
- [ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale Residual Connections for Medical Image Segmentation](https://mlsanity.com/paper/2312.10585/) (2023)
- [Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images](https://mlsanity.com/paper/2405.17520/) (2024)
- [Topological Similarity Index and Loss Function for Blood Vessel Segmentation](https://mlsanity.com/paper/2107.14531/) (2021)
- [Orientation and Context Entangled Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2207.11396/) (2022)
- [Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images](https://mlsanity.com/paper/2209.09582/) (2022)
- [RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation](https://mlsanity.com/paper/2307.06577/) (2023)
- [RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification](https://mlsanity.com/paper/2402.03166/) (2024)
- [Region Guided Attention Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2407.18970/) (2024)
- [Combining unsupervised and supervised learning for predicting the final stroke lesion](https://mlsanity.com/paper/2101.00489/) (2021)
- [Two New Stenosis Detection Methods of Coronary Angiograms](https://mlsanity.com/paper/2112.06149/) (2021)
- [SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss](https://mlsanity.com/paper/2202.09515/) (2022)
- [FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures](https://mlsanity.com/paper/2311.08059/) (2023)
- [Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning](https://mlsanity.com/paper/2409.11508/) (2024)
- [Contextual Information Enhanced Convolutional Neural Networks for Retinal Vessel Segmentation in Color Fundus Images](https://mlsanity.com/paper/2103.13622/) (2021)
- [IterMiUnet: A lightweight architecture for automatic blood vessel segmentation](https://mlsanity.com/paper/2208.01485/) (2022)
- [Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network](https://mlsanity.com/paper/2402.02184/) (2024)
- [KLDD: Kalman Filter based Linear Deformable Diffusion Model in Retinal Image Segmentation](https://mlsanity.com/paper/2410.02808/) (2024)
- [Two New Stenosis Detection Methods of Coronary Angiograms](https://mlsanity.com/paper/2108.01516/) (2021)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [Deep Learning Methods for Retinal Blood Vessel Segmentation: Evaluation on Images with Retinopathy of Prematurity](https://mlsanity.com/paper/2306.11576/) (2023)
- [Fitting tree model with CNN and geodesics to track vesselsand application to Ultrasound Localization Microscopy data](https://mlsanity.com/paper/2311.07188/) (2023)

## Abstract (arXiv)

The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. In this paper, we propose a novel method that combines the multiscale analysis provided by the Stationary Wavelet Transform with a multiscale Fully Convolutional Neural Network to cope with the varying width and direction of the vessel structure in the retina. Our proposal uses rotation operations as the basis of a joint strategy for both data augmentation and prediction, which allows us to explore the information learned during training to refine the segmentation. The method was evaluated on three publicly available databases, achieving an average accuracy of 0.9576, 0.9694, and 0.9653, and average area under the ROC curve of 0.9821, 0.9905, and 0.9855 on the DRIVE, STARE, and CHASE_DB1 databases, respectively. It also appears to be robust to the training set and to the inter-rater variability, which shows its potential for real-world applications.