# BTS-DSN: Deeply Supervised Neural Network with Short Connections for Retinal Vessel Segmentation

arXiv: https://arxiv.org/abs/1803.03963 · HTML: https://mlsanity.com/paper/1803.03963/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [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)
- [MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation](https://mlsanity.com/paper/2210.08168/) (2022)
- [Local Intensity Order Transformation for Robust Curvilinear Object Segmentation](https://mlsanity.com/paper/2202.12587/) (2022)
- [Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation](https://mlsanity.com/paper/2309.03535/) (2023)
- [Orientation and Context Entangled Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2207.11396/) (2022)
- [LMBF-Net: A Lightweight Multipath Bidirectional Focal Attention Network for Multifeatures Segmentation](https://mlsanity.com/paper/2407.02871/) (2024)
- [A Continuous Encoding-Based Representation for Efficient Multi-Fidelity Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2509.01943/) (2025)

## Abstract (arXiv)

Background and Objective: The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrhages, micro-vessel with only one to several pixels wide, and requirements for finer results. Methods:In this paper, we present a multi-scale deeply supervised network with short connections (BTS-DSN) for vessel segmentation. We used short connections to transfer semantic information between side-output layers. Bottom-top short connections pass low level semantic information to high level for refining results in high-level side-outputs, and top-bottom short connection passes much structural information to low level for reducing noises in low-level side-outputs. In addition, we employ cross-training to show that our model is suitable for real world fundus images. Results: The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods. Specially, with patch level input, the network achieved 0.7891/0.8212 sensitivity, 0.9804/0.9843 specificity, 0.9806/0.9859 AUC, and 0.8249/0.8421 F1-score on DRIVE and STARE, respectively. Moreover, our model behaves better than other methods in cross-training experiments. Conclusions: BTS-DSN achieves competitive performance in vessel segmentation task on three public datasets. It is suitable for vessel segmentation. The source code of our method is available at https://github.com/guomugong/BTS-DSN.