发表机构
Weill Cornell Medicine; Cornell University（威尔康乃尔医学院; 康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对医学图像分割标注数据有限问题，提出RegAL统一框架，通过共享拓扑感知帕累托优化，结合样本获取与未标注数据利用，沿三个互补轴评估图像，在多数据集上于极端标注稀缺时表现稳定且优于基线。
AI 中文摘要
在实际应用中，医学图像分割模型通常在标注数据有限的情况下开发，训练常始于超低标注状态。主动学习（AL）和半监督学习（SSL）虽都针对标注稀缺问题，但通常独立设计和优化，导致早期“冷启动”时目标不匹配和训练不稳定。我们提出RegAL，一个统一的主动半监督框架，由共享的拓扑感知帕累托优化控制，将样本获取与未标注数据利用相结合。RegAL沿三个互补轴评估图像，以选择有解剖学信息的边缘情况进行标注，并识别几何稳定的图谱候选进行增强，以训练自监督的平均教师分割网络。在多个数据集上，RegAL在标注极少时仍保持稳定，且在多个指标上优于现有基线。
英文摘要
In practical settings, medical image segmentation models are often developed with limited annotated data rather than fully labeled datasets. Training frequently begins in ultra-low labeled regimes where only a small number of volumes are annotated. In such scenarios, practitioners must simultaneously decide which cases to annotate and how to best use the remaining unlabeled data. Although active learning (AL) and semi-supervised learning (SSL) both target annotation scarcity, they are typically designed and optimized independently, resulting in objective mismatch and unstable training during early-stage "cold start" conditions. We propose RegAL, a unified active semi-supervised framework governed by a shared topology-aware Pareto optimization that couples sample acquisition with unlabeled data utilization. RegAL evaluates images along three complementary axes, voxel-wise uncertainty, feature diversity, and a novel topological consistency metric, to select anatomically informative edge cases for annotation. On the other hand, the same criteria are used to identify geometrically stable atlas candidates for diffeomorphic registration-guided augmentation to train a self-supervised Mean Teacher segmentation network. Across BraTS 2021, dHCP, and ProstateX, RegAL remains stable with few labeled volumes and consistently outperforms state-of-the-art AL, SSL, and active semi-supervised baselines across Dice and boundary-distance (ASD, HD95) metrics under extreme annotation scarcity.
