发表机构
Balgrist University Hospital; Zurich University of Applied Sciences (ZHAW)（巴尔格里斯特大学医院; 苏黎世应用科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MedSAM2-Anatomy是一种无训练的推理时优化框架，通过融合专家模型生成的自动解剖提示，提升冻结分割模型的髋部、肩部肌肉骨骼分割性能，无需人工提示或模型重训。
AI 中文摘要
从CT和MRI中对髋部和肩部解剖结构进行高分辨率3D分割是外科手术规划的关键，但冻结的分割模型在领域偏移下常表现不佳。基于CNN的专家模型可全自动运行但缺乏适应性，而可提示的基础模型泛化能力更强却需要人工提示。我们提出MedSAM2-Anatomy，这是一种无训练的推理时优化框架，无需重新训练或人工交互即可提升冻结分割模型的性能。冻结的专家模型生成解剖先验，这些先验会自动转换为针对冻结3D基础模型的多个提示假设；候选掩码被融合，而解剖学上不合理的先验则被剔除。整个过程不更新任何模型权重，也无需人工提示。我们选取TotalSegmentator作为代表性专家模型、MedSAM2作为代表性基础模型，从而可单独分析推理策略的贡献。在独立的Balgrist-V0 CT和MRI队列上的评估显示，该推理时优化将髋部MRI的中位Dice从0.71提升至0.92，肩部CT的中位Dice从0.89提升至0.92；同时将髋部MRI的中位HD95从22.0 mm降至5.0 mm。在公开的TotalSegmentator基准测试中，专家模型仍保持最优性能，这表明最优融合策略取决于专家先验的可靠性。这些结果表明，无训练的推理时优化为提升冻结分割模型的性能提供了实用策略，且无需人工提示。
英文摘要
High-resolution 3D segmentation of hip and shoulder anatomy from CT and MRI is essential for surgical planning, yet frozen segmentation models often fail under domain shift. CNN-based expert models are fully automatic but lack adaptability, whereas promptable foundation models generalize better but require manual prompting. We present MedSAM2-Anatomy, a training-free inference-time optimization framework that improves frozen segmentation models without retraining or human interaction. A frozen expert model generates anatomical priors that are automatically converted into multiple prompt hypotheses for a frozen 3D foundation model. Candidate masks are fused while anatomically implausible priors are rejected. No model weights are updated and no manual prompts are required. TotalSegmentator and MedSAM2 are used as representative expert and foundation models, allowing the contribution of the inference policy to be isolated. Evaluation on the independent Balgrist-V0 CT and MRI cohorts shows that inference-time optimization increases median Dice from 0.71 to 0.92 on hip MRI and from 0.89 to 0.92 on shoulder CT, while reducing median HD95 on hip MRI from 22.0 mm to 5.0 mm. On public TotalSegmentator benchmarks, the expert model remains strongest, indicating that the optimal fusion strategy depends on the reliability of the expert prior. These results demonstrate that training-free inference-time optimization provides a practical strategy for improving frozen segmentation models without manual prompting.
CommentsOriginal research manuscript (13 pages, 4 figures, 2 tables). No prior publication
