发表机构
Nanjing University of Science and Technology（南京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究极端半监督下视网膜血管分割问题，提出ESRVS方法，通过选择参考图像、传递血管线索、构建原型等操作生成伪标签并优化监督，在多数据集上表现优异，证明基础模型标签传播对高效视网膜血管分割有潜力。
AI 中文摘要
在医学图像分析中，从最少的人工监督中学习是一个长期目标，因为密集的专家标注成本高昂。我们研究了在极端半监督设置下的视网膜血管分割，即使用一张标注图像和一组未标注图像。我们提出了ESRVS，它选择一个有代表性的参考图像进行手动标注，并使用适应目标域的DINOv3特征传递血管线索。ESRVS构建了一个多粒度血管原型，将原型相似性图与物理启发的先验相结合以生成初始伪标签，并通过加权伪标签训练和对抗性细化来优化传递的监督。在八个公共数据集上，ESRVS在六个数据集上实现了最佳的Dice和clDice，在所有八个数据集上实现了最佳的HD95，优于其他半监督方法，尽管那些方法使用了10%到20%的标注数据。与Mask2Former相比，ESRVS平均保留了93.7%的全监督Dice和95.1%的全监督clDice。这些结果证明了基础模型标签传播在高效标签视网膜血管分割中的潜力。代码可在该https网址获取。
英文摘要
Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly. We study retinal vessel segmentation in an extreme semi-supervised setting with one annotated image and a pool of unlabeled images. We propose ESRVS, which selects a representative reference image for manual annotation and transfers vessel cues using target-domain-adapted DINOv3 features. ESRVS constructs a multi granular vessel prototype, combines prototype-similarity maps with a physics-inspired prior to generate initial pseudo-labels, and refines the transferred supervision through weighted pseudo-label training and adversarial refinement. Across eight public datasets, ESRVS achieves the best Dice and clDice on six datasets, and the best HD95 on all eight datasets among the compared semi-supervised methods, although those methods use 10 to 20% labeled data. With Mask2Former, ESRVS retains on average 93.7% of fully supervised Dice and 95.1% of fully supervised clDice. These results demonstrate the potential of foundation-model label propagation for highly label-efficient retinal vessel segmentation. Code is available at https://github.com/IAANNH/ESRVS.
Comments15 pages, 2 figures.Accepted by PRCV 2026
