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Byong Su Choi^1,2,3, Chris J Beltran¹, Sven Olberg⁴

¹Department of Radiation Oncology, Mayo Clinic, Florida, USA.

Medical physics

|August 21, 2024

概括

个性化超空间学习 (PHL) -IDOL框架通过对患者特定数据进行超拟合模型来改善适应性放射治疗 (ART) 的医疗图像细分,提高准确性和减少轮时间.

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:

适应性放射治疗 (ART) 旨在精确的剂量输送和组织节约,但由于耗时的手动重塑而受到阻碍.
基于深度学习的细分 (DLS) 显示了自动化轮的前景,但需要大量高质量的数据集来实现通用化.
现有的DLS方法在临床实施中扎,原因是数据策划和实现患者特定准确性的挑战.

研究的目的:

引入个性化超空间学习 (PHL) -IDOL框架,以增强ART工作流程的自动细分.
通过允许对特定患者特征进行过拟合来解决先前的故意深度过拟合学习 (IDOL) 的局限性.
创建针对患者的特定数据集,以提高医疗图像细分中的模型性能.

主要方法:

一个两阶段的培训过程:首先,在各种患者数据 (n=100) 上训练一个一般的DLS模型.
然后,通过选择类似的患者 (基于MSE,PSNR,SSIM,UQI) 并扭曲其轮,使用个性化的数据集来微调一般模型.
通过比较子相似系数 (DSC) 和豪斯多夫距离95% (HD95%) 与一般,连续和传统的IDOL模型,在20名测试患者中使用18个结构来评估性能.

主要成果:

PHL-IDOL框架显著改善了细分性能,实现了0.87的Dice平均得分 (与其他模型的0.81-0.83相比).
使用PHL-IDOL,豪斯多夫距离95%降至2.36,优于其他方法 (3.06-2.79).
与一般模型相比,PHL-IDOL的性能指标标准偏差几乎减少了一半,这表明一致性增加.

结论:

与一般的DLS方法相比,PHL-IDOL框架显示出优越的自动细分性能.
通过PHL-IDOL利用患者特定信息对于推进在线自适应性放射治疗工作流程至关重要.
这种方法在提高ART的效率和准确性方面具有显著的前景.

关键词:

艺术艺术艺术艺术汽车细分汽车细分深度学习是一种深度学习.头部和部头部和部过度适应过度适应

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