Interactive segmentation enables clinicians to guide annotation, but existing zero-shot models like nnInteractive fail to consistently reach expert-level performance across diverse medical imaging tasks. Because annotation campaigns produce a growing stream of task-specific labelled data, online adaptation of the segmentation model is a natural complement to zero-shot inference. We propose CLoPA, a continual adaptation strategy that tunes a small fraction of nnInteractive's parameters on the annotation cache, triggered by lightweight episode scheduling. CLoPA requires no new parameters or changes to the inference pipeline, and operates entirely within the existing annotation workflow. Across eight Medical Segmentation Decathlon tasks spanning diverse anatomical targets and imaging characteristics, CLoPA rapidly elevates performance to expert-level, even for tasks where nnInteractive previously failed, with the majority of gains realised after a single training episode. We show that the benefits of tuning different parameter groups depends on task characteristics and data regimes. Also, that for targets with complex geometries (e.g., hepatic vessels), instance normalisation and low-level feature tuning saturates, suggesting a need for deeper feature-representation alignment in the most challenging scenarios.

翻译：交互式分割使临床医生能够引导标注，但现有零样本模型（如nnInteractive）难以在多样化的医学成像任务中持续达到专家级性能。由于标注活动会产生持续增长的任务特定标注数据流，对分割模型进行在线自适应自然成为零样本推理的补充。我们提出CLoPA——一种连续自适应策略，该策略通过轻量级片段调度机制触发，仅对nnInteractive的少量参数在标注缓存上进行微调。CLoPA无需引入新参数或修改推理流程，完全在现有标注工作流中运行。在涵盖不同解剖目标和成像特征的八项医学分割十项全能任务中，CLoPA能快速将性能提升至专家水平（即使在nnInteractive先前失败的任务中），且大部分性能增益在单次训练片段后即可实现。我们发现不同参数组的调优效果取决于任务特性和数据状态。同时，对于具有复杂几何结构的目标（如肝血管），实例归一化和低级特征调优会达到饱和，这表明在最具挑战性的场景中需要更深层次的特征表示对齐。