发表机构
Johns Hopkins University; University of Minnesota（约翰霍普金斯大学; 明尼苏达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对交互式三维肺肿瘤分割，提出LeCor方法，将每次修正作为训练信号，通过元学习测试时训练病例适配器，显著提升未点击切片的准确率并减少修正轮次。
AI 中文摘要
在计算机断层扫描（CT）上勾画肺肿瘤轮廓占用了放射治疗计划中相当多的时间，而由模型提出的轮廓可由临床医生进行交互式细化。诸如SAM 3之类的可提示基础模型通过将每次修正写入会话记忆来支持这一工作流程，该记忆影响剩余切片，而模型权重保持固定。在来自五个公共CT队列的690个测试病例上，针对肺肿瘤微调SAM 3将单点提示获得的Dice从0.298提高到0.757，七轮修正进一步将其提高到0.765，但仅依靠记忆条件化，标注者未触及的切片上的准确率在六轮后停止提升。因此，我们将每次修正视为训练信号，并提出LeCor，它对一组小型病例适配器执行测试时训练，这些适配器对每个病例重置，并通过元学习使得由点击驱动的单次梯度步长能改善未被点击的切片。在跨越至少八个切片的133个测试病例上，LeCor将七轮修正后达到的Dice从微调模型的0.787提高到0.827，将从未达到Dice 0.80的病例数从47减少到27，并在三轮修正时达到微调模型七轮修正所达到的准确率。
英文摘要
Delineating lung tumours on computed tomography (CT) takes a considerable share of the time spent on radiotherapy planning, and a contour proposed by a model can be refined interactively by the clinician. Promptable foundation models such as SAM 3 support this workflow by writing each correction into a session memory that conditions the remaining slices, while the model weights stay fixed. On 690 test cases from five public CT cohorts, fine-tuning SAM 3 on lung tumours raises the Dice obtained from a single point prompt from 0.298 to 0.757, and seven rounds of corrections raise it further to 0.765, but under memory conditioning alone the accuracy on slices the annotator has not touched stops improving after six rounds. We therefore treat each correction as a training signal and propose LeCor, which performs test-time training on a small set of case adapters that are reset for every case and meta-learned such that a single gradient step driven by a click improves the slices that were not clicked. On the 133 test cases that span at least eight slices, LeCor raises the Dice reached after seven correction rounds from 0.787 with the fine-tuned model to 0.827, reduces the number of cases that never reach a Dice of 0.80 from 47 to 27, and reaches in three correction rounds the accuracy that the fine-tuned model attains in seven.
Comments18 pages, 4 figures
