  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.
