这篇论文用SAM加人工迭代的方法做肺结节分割，Dice近85%，比13种SAM变体都强，非医学人员培训后也能干医学标注的活。
Hi-Seg是一种基于SAM的人机循环分割框架，用于肺结节CT图像分割。研究使用了来自12个中心1179名患者的胸部CT扫描进行外部验证。所有标注者组平均Dice得分接近85%，优于5个最先进的深度学习模型（10-22%）和13个SAM变体（1-29%）。经过短期训练的非医学标注者达到了与初级医学生相当的性能。该工作表明人机循环分割可减少临床医生工作量并实现可扩展的众包标注。
Human and AI collaboration for pulmonary nodule segmentation
Medical expert annotators are scarce, and blind reliance on artificial intelligence (AI) can be misleading, motivating approaches in which humans, particularly junior medical trainees or even non-medical personnel, collaborate with AI to achieve robust medical segmentation. Although the Segment Anything Model (SAM) shows promise for general-purpose image segmentation, its performance in human-AI collaboration for specialized medical tasks has not been thoroughly evaluated. Here we present Hi-Seg, a human-in-the-loop segmentation framework for pulmonary nodules built on SAM. Humans iteratively refine prompts through trial-and-error learning and semantic reasoning, progressively guiding SAM toward higher-quality masks. Using chest CT scans from 1,179 patients across 12 centers, we conducted the first large-scale external validation of collaborative human-SAM segmentation. Across all annotator groups, Hi-Seg achieved a mean Dice score of almost 85%, outperforming five state-of-the-art deep learning models by 10-22% and 13 SAM variants by 1-29%. Hi-Seg improved segmentation accuracy while reducing annotation time for medical annotators, and briefly trained non-medical annotators achieved performance comparable to that of the junior medical student. These findings suggest that human-in-the-loop segmentation can reduce clinician workload, enable scalable crowdsourced annotation, and transform clinical workflows by facilitating the safe and efficient integration of foundation models into routine clinical practice.