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通过卷积神经网络CT的多器官细分:培训设置和扫描仪制造商的影响
Amy J Weisman1, Daniel T Huff1, Rajkumar Munian Govindan1
1AIQ Solutions, Madison, WI, United States of America.
Biomedical physics & engineering express
|September 19, 2023
概括
在多个器官和扫描仪类型上训练的多类卷积神经网络 (CNN) 在CT扫描上实现了卓越的自动化器官细分,提高了临床软件定量分析的效率和准确性.
科学领域:
- 医学成像分析分析 医学成像分析
- 放射学中的人工智能
- 定量成像技术的使用
背景情况:
- 在CT图像上的自动化器官细分对于临床实践中的定量软件设备至关重要.
- 在广泛采用自动化细分之前,了解模型性能敏感性至关重要.
- 目前的手动细分是耗时和劳动密集型的,阻碍了临床应用.
研究的目的:
- 为了研究单类和多类卷积神经网络 (CNN) 之间的器官细分性能差异.
- 在单一制造商和多个制造商的CT扫描中训练CNN的性能进行比较.
- 为了评估多类,多制造商CNN的效率和准确性,用于自动化器官细分.
主要方法:
- 一个多类CNN被训练在413个全身PET/CT扫描来自西门子,通用电气和菲利普斯扫描仪,细分16个器官.
- 多类CNN与在同一数据集上训练的16个单类CNN进行了比较.
- 使用西门子和通用电气数据,将制造商特定的CNN (仅西门子,仅GE) 与制造商混合的CNN进行了比较.
主要成果:
- 多类CNN表现出高性能,在大多数器官中表现优于单类CNN,并实现了卓越的效率.
- 多类模型对16个器官进行了细分,比16个单一类模型要快得多.
- 与制造商特定培训相比,制造商混合培训的成绩略高.
结论:
- 一个单一的多类CNN训练在不同的CT数据 (多个器官,多个制造商) 提供高质量的自动器官细分.
- 这种方法显著提高了效率,解决了临床定量分析手动细分的局限性.
- 这种模型对于在治疗反应评估的软件设备中实现先进的图像处理至关重要.
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