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从放射治疗患者的对联MRI使用CT上的SynthSeg细分性能的验证

Selena Huisman¹, Matteo Maspero², Marielle Philippens³

¹Department of Radiation Oncology, Amsterdam UMC, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; Department of Radiation Oncology, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands.

|November 18, 2024

概括

SynthSeg在CT扫描上提供自动化大脑细分,显示了神经解剖学研究的潜力,尽管精度低于MRI. 质量控制可以排除低性能细分.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经科学是一个神经科学.

背景情况:

医疗图像的手动细分是耗时和困难的,特别是图像质量差的图像质量.
需要自动化解决方案,特别是对于患病的大脑.
SynthSeg是一个深度学习模型,用于在各种图像对比度和分辨率上自动对大脑进行细分.

研究的目的:

在计算机断层扫描 (CT) 图像上验证 SynthSeg 强大的大脑细分模型.
在CT和MRI扫描的多中心数据集上评估SynthSeg的性能.
为了比较基于CT的细分与基于MRI的细分,使用Dice分数和Hausdorff 95距离.

主要方法:

利用5个中心的放射治疗患者的260个配对CT和MRI扫描的开放访问数据集.
应用SynthSeg用于CT和MRI上的大脑细分,将MRI细分视为基本事实.
通过使用子分数和豪斯多夫95距离 (HD95) 评估细分,并使用自动化质量控制 (QC) 来排除表现不佳的地区.

主要成果:

子的中位数为0.76,平均体积差异为7.79% (CT细分小于MRI).
中位数HD95为2.95毫米.
自动化QC提高了Dice的中位数0.1和HD95的中位数0.05毫米.
基于CT的分析复制了MRI检测到的与性别和年龄相关的形态差异.

结论:

在非精度关键的应用中,SynthSeg可用于基于CT的自动大脑细分.
CT性能低于MRI,但基于QC的值可以排除低质量的细分.
基于CT的神经解剖学研究是可行的,显示与MRI类似的基于性别和年龄的相关性.

关键词:

临床大脑CT 扫描图临床大脑磁力共振成像 (MRI)深度学习是一种深度学习.验证验证验证验证

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