# Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing

arXiv: https://arxiv.org/abs/2005.13337 · HTML: https://mlsanity.com/paper/2005.13337/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 4 of 4)

- [Learning to Address Intra-segment Misclassification in Retinal Imaging](https://mlsanity.com/paper/2104.12138/) (2021)
- [DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2212.14615/) (2022)
- [VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation](https://mlsanity.com/paper/2203.06425/) (2022)
- [Artery-Vein Segmentation from Fundus Images using Deep Learning](https://mlsanity.com/paper/2510.03717/) (2025)

## Abstract (arXiv)

Retinal imaging serves as a valuable tool for diagnosis of various diseases. However, reading retinal images is a difficult and time-consuming task even for experienced specialists. The fundamental step towards automated retinal image analysis is vessel segmentation and artery/vein classification, which provide various information on potential disorders. To improve the performance of the existing automated methods for retinal image analysis, we propose a two-step vessel classification. We adopt a UNet-based model, SeqNet, to accurately segment vessels from the background and make prediction on the vessel type. Our model does segmentation and classification sequentially, which alleviates the problem of label distribution bias and facilitates training. To further refine classification results, we post-process them considering the structural information among vessels to propagate highly confident prediction to surrounding vessels. Our experiments show that our method improves AUC to 0.98 for segmentation and the accuracy to 0.92 in classification over DRIVE dataset.