加强在线自适应性放射治疗,以不确定性为基础的细分错误和分布外检测
Marissa van Lente1,2, Josien Pluim1, Samuel Fransson3,4
1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Frontiers in oncology
|January 30, 2026
概括
深度学习细分的不确定性估计与前列腺癌放射治疗的准确性相关. 这种方法可以识别细分质量,并区分在分销和分销之外的数据.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 人工智能在医学中的应用
背景情况:
- 解剖细分引入了在线适应性放射治疗中的重大不确定性.
- 深度学习 (DL) 模型越来越多地用于放射治疗工作流程的细分.
- 精确的细分对于精确的辐射输送和患者安全至关重要.
研究的目的:
- 调查从DL细分和细分精度估计的不确定性之间的关系.
- 评估不确定性估计检测分布外 (OOD) 数据的能力.
- 评估不确定性估计对适应性放射治疗质量控制的有用性.
主要方法:
- 应用蒙特卡洛脱落到DL模型,用于从MRI导向放射治疗中对前列腺癌图像 (临床目标体积,膀,直肠) 进行细分.
- 利用预测 (PE) 来量化模型和数据的不确定性,建立一个值来分类细分为"确定"或"不确定".
- 使用相互信息 (MI) 来区分在分布 (ID) 和OOD数据,使用健康志愿者的MRI扫描.
主要成果:
- DL细分模型获得了高的Dice分数 (例如,膀的94.8%).
- 较高的PE值与细分边界和不正确的预测相关,使得错误的检测成为可能.
- 相互信息实现了ID和OOD数据之间的100%分离,证明了对意外输入的稳定性.
结论:
- 针对MR导向前列腺癌放射治疗的DL细分的不确定性估计与细分精度 (迪斯分数) 相对应.
- 这种方法对适应性放射治疗工作流中的细分的实时质量评估具有前景.
- 初步发现表明,不确定性估计可以有效地区分预期的 (ID) 和意想不到的 (OOD) 数据.
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