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基于深度学习的肺体积估计与动态胸部X射线扫描

Nozomi Ishihara¹, Rie Tanaka¹, Haruto Kikuno¹

¹College of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Ishikawa, Japan.

Journal of applied clinical medical physics

|January 29, 2026

概括

像VGG19和DenseNet121这样的深度学习模型从动态胸部X射线图像 (DCR) 中准确估计肺部体积. 这种先进的方法超过了肺功能评估的传统线性回归.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
肺功能测试试验肺功能测试

背景情况:

动态胸部放射 (DCR) 是一种新兴的低剂量成像技术,用于评估肺功能.
DCR在呼吸过程中捕获连续的X射线图像,为肺体积变化提供了洞察力.

研究的目的:

评估深度学习模型,以从DCR图像中估计肺部体积.
将深度学习方法的准确性与传统估计技术进行比较.

主要方法:

两个卷积神经网络 (CNN),VGG19和DenseNet121,在257名患者的DCR数据集上接受了培训.
参考肺体积来自计算机断层扫描 (CT) 扫描;使用MAE,MAPE和Pearson相关系数 (r) 评估模型性能.

主要成果:

在整个肺体积估计中,VGG19和DenseNet121模型的表现明显优于线性回归 (例如,VGG19:MAE 373 mL,r 0.88;DenseNet121:MAE 376 mL,r 0.90).
线性回归显示出更高的误差 (MAE 568 mL,r 0.84).
使用CNN的DCR衍生的强迫生命能力 (FVC) 估计显示了中等的相关性,但相对错误较高.

结论:

与线性回归相比,深度学习方法提供了一个更准确的方法来估计DCR的肺体积,而不是线性回归.
未来的研究将重点放在CNN架构和指导呼吸机动上,这将进一步提高DCR在肺功能测试中的实用性.

关键词:

深度学习是一种深度学习.动态胸部X射线图动态胸部X射线图肺的体积肺的体积肺的体积

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