一种具有大型视觉模型的细分方法,用于磁共振成像指导的自适应性放射治疗
Kuo Men1, Bining Yang1, Yuxiang Liu1
1National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Medical physics
|January 30, 2026
概括
这项研究引入了SAM-ART,一种新的AI模型,通过整合患者特定数据,显著提高了MRI引导放射治疗细分精度. 该方法减少了手工劳动,实现了90%以上的可接受细分,对临床使用进行了最小的修订.
科学领域:
- 医疗成像医学成像
- 辐射疗法 辐射疗法
- 人工智能的人工智能
背景情况:
- 细分是MRI指导适应性放射治疗 (MRIgART) 的一个耗时的步骤.
- 现有的模型,如分段任何模型 (SAM) 需要广泛的手动输入 (点击,边界框) 医疗成像细分.
- 这限制了先进AI在临床工作流程中的效率和适用性.
研究的目的:
- 介绍SAM-ART,这是一个大型视觉模型,旨在提高MRIgART细分精度.
- 将个性化的患者信息集成到SAM框架中,以提高性能.
- 为了减少准确医疗图像细分所需的手动交互.
主要方法:
- 从38名前列腺癌和10名直肠癌患者中利用计划CT (pCT),批准轮和每日MRI (dMRI).
- 开发了具有图像编码器,提示编码器和面具解码器的SAM-ART.
- 使用的面具和盒子提示来自pCT轮,通过可变形图像注册 (DIR) 传播到dMRI,包含患者特定数据.
主要成果:
- SAM-ART实现了0.934 ± 0.023的平均子相似系数 (DSC),超过了DIR (0.873 ± 0.063) 和传统的深度学习 (tDL) (0.887 ± 0.056).
- 与单独点 (0.910) 或盒 (0.921) 提示相比,面具/盒提示产生了更好的结果 (DSC 0.934).
- 89.38%的细分符合临床接受标准 (DSC ≥0.85,豪斯多夫距离 ≤5mm,平均距离达成协议 ≤1.5mm),最大限度地减少了手动校正需求.
结论:
- 开发了一种基于SAM的新细分方法,整合了个性化信息和优化提示.
- 拟议的SAM-ART在MRIgART的细分精度方面显著超过了基线方法.
- 该方法大大减少了手动轮修改的需要,简化了临床工作流程.
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