# Convolutional Prompting for Broad-Domain Retinal Vessel Segmentation

arXiv: https://arxiv.org/abs/2412.18089 · HTML: https://mlsanity.com/paper/2412.18089/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks](https://mlsanity.com/paper/1912.05763/) (2019)
- [Knapsack Pruning with Inner Distillation](https://mlsanity.com/paper/2002.08258/) (2020)
- [Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels](https://mlsanity.com/paper/2103.03451/) (2021)
- [Convolutional Visual Prompt for Robust Visual Perception](https://mlsanity.com/paper/2303.00198/) (2023)
- [An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset](https://mlsanity.com/paper/2309.09483/) (2023)
- [LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation](https://mlsanity.com/paper/2107.04282/) (2021)
- [Unsupervised Domain Expansion for Visual Categorization](https://mlsanity.com/paper/2104.00233/) (2021)
- [Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images](https://mlsanity.com/paper/2305.08078/) (2023)

## Built on by (most cited first, 1 of 1)

- [Cross-modal Fundus Image Registration under Large FoV Disparity](https://mlsanity.com/paper/2512.12657/) (2025)

## Abstract (arXiv)

Previous research on retinal vessel segmentation is targeted at a specific image domain, mostly color fundus photography (CFP). In this paper we make a brave attempt to attack a more challenging task of broad-domain retinal vessel segmentation (BD-RVS), which is to develop a unified model applicable to varied domains including CFP, SLO, UWF, OCTA and FFA. To that end, we propose Dual Convoltuional Prompting (DCP) that learns to extract domain-specific features by localized prompting along both position and channel dimensions. DCP is designed as a plug-in module that can effectively turn a R2AU-Net based vessel segmentation network to a unified model, yet without the need of modifying its network structure. For evaluation we build a broad-domain set using five public domain-specific datasets including ROSSA, FIVES, IOSTAR, PRIME-FP20 and VAMPIRE. In order to benchmark BD-RVS on the broad-domain dataset, we re-purpose a number of existing methods originally developed in other contexts, producing eight baseline methods in total. Extensive experiments show the the proposed method compares favorably against the baselines for BD-RVS.