在MRI指导放射治疗中,GTV细分与提示型基础模型
Tom Julius Blöcker1, Nikolaos Delopoulos1, Miguel A Palacios2
1Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
Physics in medicine and biology
|December 16, 2025
概括
快速基础模型在MRI导向放射治疗中显示出细分瘤总体积 (GTV) 的前景. 像nnInteractive和MedSAM2这样的模型取得了与专业方法相比或优于专业方法的结果,表明了更广泛的临床应用的潜力.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 辐射疗法 辐射疗法
背景情况:
- 在日常MRI上准确地划分瘤总体积 (GTV),对于MRI导向放射治疗至关重要.
- 有专门的模型存在,但通常是针对瘤的.
- 迅速的基础模型为多功能GTV细分提供了潜在的替代方案.
研究的目的:
- 研究MRI导向放射治疗中GTV细分的提示性基础模型的有效性.
- 为了比较不同瘤部位的不同提示模型和提示类型的性能.
- 评估这些模型的潜力,作为对特定领域细分方法的替代方案.
主要方法:
- 使用六种稀疏的几何提示类型 (点,框,二维面罩) 评估了提示型基础模型 (SAM2,MedSAM2,nnInteractive).
- 在MRI扫描上使用了来自不同解剖部位 (腹部,肺部,肝脏,胰腺,骨盆) 的临床GTV口罩的多机构数据集.
- 模型的性能使用包括子相似系数 (DSC) 在内的指标进行评估.
主要成果:
- 提示型模型产生了与特定领域模型相比或优于该领域的细分面罩,中位数DSC高达0.85.
- 与SAM2 (0.54) 相比,nnInteractive和MedSAM2表现出更高的性能 (平均DSC分别为0.75和0.70).
- 具有更大的空间信息的提示通常会改善结果,尽管这种影响对于nnInteractive和MedSAM2.2来说不那么明显.
结论:
- 快速基础模型有可能在MRI中对多种瘤类型进行GTV细分.
- 需要进一步的研究来提高模型性能并减少临床实施的输出差异.
- 这些模型代表了提高放射治疗规划效率和准确性的有希望的途径.
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