# Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification

arXiv: https://arxiv.org/abs/2007.09337 · HTML: https://mlsanity.com/paper/2007.09337/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification](https://mlsanity.com/paper/2103.09097/) (2021)
- [Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation](https://mlsanity.com/paper/2105.14732/) (2021)
- [RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging](https://mlsanity.com/paper/2203.14928/) (2022)
- [Learning to Address Intra-segment Misclassification in Retinal Imaging](https://mlsanity.com/paper/2104.12138/) (2021)
- [Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images](https://mlsanity.com/paper/2209.09582/) (2022)
- [RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation](https://mlsanity.com/paper/2309.15638/) (2023)
- [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)
- [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)
- [Fully Automated Tree Topology Estimation and Artery-Vein Classification](https://mlsanity.com/paper/2202.02382/) (2022)
- [Fully Automated Artery-Vein ratio and vascular tortuosity measurement in retinal fundus images](https://mlsanity.com/paper/2301.01791/) (2023)
- [Improve Retinal Artery/Vein Classification via Channel Couplin](https://mlsanity.com/paper/2508.03738/) (2025)

## Abstract (arXiv)

Retinal artery/vein (A/V) classification plays a critical role in the clinical biomarker study of how various systemic and cardiovascular diseases affect the retinal vessels. Conventional methods of automated A/V classification are generally complicated and heavily depend on the accurate vessel segmentation. In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation. The input module of the network integrates the domain knowledge of widely used retinal preprocessing and vessel enhancement techniques. We specially customize the output block of the network with a spatial activation mechanism, which takes advantage of a relatively easier task of vessel segmentation and exploits it to boost the performance of A/V classification. In addition, deep supervision is introduced to the network to assist the low level layers to extract more semantic information. The proposed network achieves pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification accuracy of 94.50%, which is the state-of-the-art performance for both tasks on the AV-DRIVE dataset. Furthermore, we have also tested the model performance on INSPIRE-AVR dataset, which achieves a skeletal A/V classification accuracy of 91.6%.