# A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation

arXiv: https://arxiv.org/abs/1611.02064 · HTML: https://mlsanity.com/paper/1611.02064/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2016.

## Built on by (most cited first, 12 of 12)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning](https://mlsanity.com/paper/2304.12856/) (2023)
- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images](https://mlsanity.com/paper/2309.05780/) (2023)
- [Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images](https://mlsanity.com/paper/2209.09582/) (2022)
- [PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation](https://mlsanity.com/paper/2108.11695/) (2021)
- [A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers](https://mlsanity.com/paper/2111.03853/) (2021)
- [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)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [Application Of ADNN For Background Subtraction In Smart Surveillance System](https://mlsanity.com/paper/2301.00264/) (2022)
- [A Deep Convolutional Neural Network-Based Novel Class Balancing for Imbalance Data Segmentation](https://mlsanity.com/paper/2506.18474/) (2025)

## Abstract (arXiv)

Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score.