基于卷积神经网络的体积估计,来自低剂量未增强的计算机断层扫描
Lukas Müller1, Dativa Tibyampansha2, Peter Mildenberger1
1Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst, 1, 55131, Mainz, Germany.
BMC medical imaging
|November 16, 2023
概括
使用卷积神经网络 (CNN) 模型的自动体积估计在低剂量CT扫描中是快速而准确的. 这种方法改进了治疗病的传统技术.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 腎病管理 腎病管理
背景情况:
- 精确的体积测量对于诊断和管理病至关重要.
- 目前用于体积测定的半自动化方法耗时且容易出现错误.
- 自动化方法通常需要增强对比度的CT扫描,这限制了它们的适用性.
研究的目的:
- 开发一种自动化的方法来估计体积,使用非对比,低剂量CT扫描.
- 评估卷积神经网络 (CNN) 模型的可行性和准确性,用于尿病患者的细分.
- 为当前的体积估计技术提供更快,更可复制的替代方案.
主要方法:
- 在2D CNN模型中,从210名患者的低剂量,未经增强的CT扫描中,对手工分割的脏图像进行了训练.
- 用子相似系数 (DSC) 评估细分的准确性.
- 自动化方法与放射科医生对22个未见病例的半自动化测量得到了验证.
主要成果:
- 在CNN启用的体积估计中,每次扫描平均为两个脏32秒.
- 实现了高分段精度,左的DSC值为0.91,右的DSC值为0.86.
- 在自动化和半自动化量估计之间发现了很好的一致性 (ICC > 0.89).
结论:
- 支持CNN的体积估计是一种可行且高度可复制的技术.
- 这种自动化方法即使在低剂量,非增强型CT扫描中也是有效的.
- 自动脏细分可以显著提高放射学报告的定量价值.
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