# Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset

arXiv: https://arxiv.org/abs/2406.13645 · HTML: https://mlsanity.com/paper/2406.13645/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [Active Adversarial Domain Adaptation](https://mlsanity.com/paper/1904.07848/) (2019)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling](https://mlsanity.com/paper/2109.09735/) (2021)
- [Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation](https://mlsanity.com/paper/2303.10326/) (2023)
- [Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals](https://mlsanity.com/paper/2309.13401/) (2023)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher](https://mlsanity.com/paper/2307.09973/) (2023)
- [Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs](https://mlsanity.com/paper/2207.11102/) (2022)
- [A Survey on Domain Generalization for Medical Image Analysis](https://mlsanity.com/paper/2402.05035/) (2024)
- [MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2403.11689/) (2024)
- [Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation](https://mlsanity.com/paper/2308.07731/) (2023)
- [Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network](https://mlsanity.com/paper/2004.12776/) (2020)

## Built on by (most cited first, 11 of 11)

- [Cross-conditioned Diffusion Model for Medical Image to Image Translation](https://mlsanity.com/paper/2409.08500/) (2024)
- [Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model](https://mlsanity.com/paper/2409.04356/) (2024)
- [Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization](https://mlsanity.com/paper/2410.07901/) (2024)
- [Language-Driven Interactive Shadow Detection](https://mlsanity.com/paper/2408.08543/) (2024)
- [Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network](https://mlsanity.com/paper/2410.14965/) (2024)
- [An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation](https://mlsanity.com/paper/2407.02893/) (2024)
- [FunOTTA: On-the-Fly Adaptation on Cross-Domain Fundus Image via Stable Test-time Training](https://mlsanity.com/paper/2407.04396/) (2024)
- [VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2601.10124/) (2026)
- [MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation](https://mlsanity.com/paper/2511.02193/) (2025)
- [Learning What is Worth Learning: Active and Sequential Domain Adaptation for Multi-modal Gross Tumor Volume Segmentation](https://mlsanity.com/paper/2508.20528/) (2025)
- [Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning](https://mlsanity.com/paper/2509.10784/) (2025)

## Abstract (arXiv)

Accurate vessel segmentation in Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images is crucial for diagnosing retinal diseases. Although recent techniques have shown encouraging outcomes in vessel segmentation, models trained on one medical dataset often underperform on others due to domain shifts. Meanwhile, manually labeling high-resolution UWF-SLO images is an extremely challenging, time-consuming and expensive task. In response, this study introduces a pioneering framework that leverages a patch-based active domain adaptation approach. By actively recommending a few valuable image patches by the devised Cascade Uncertainty-Predominance (CUP) selection strategy for labeling and model-finetuning, our method significantly improves the accuracy of UWF-SLO vessel segmentation across diverse medical centers. In addition, we annotate and construct the first Multi-center UWF-SLO Vessel Segmentation (MU-VS) dataset to promote this topic research, comprising data from multiple institutions. This dataset serves as a valuable resource for cross-center evaluation, verifying the effectiveness and robustness of our approach. Experimental results demonstrate that our approach surpasses existing domain adaptation and active learning methods, considerably reducing the gap between the Upper and Lower bounds with minimal annotations, highlighting our method's practical clinical value. We will release our dataset and code to facilitate relevant research: https://github.com/whq-xxh/SFADA-UWF-SLO.