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优化卷积神经网络用于临床计算机断层扫描成像中的慢性阻塞性肺病检测

Tina Dorosti¹, Manuel Schultheiss¹, Felix Hofmann²

¹Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, 85748, Bavaria, Germany; Munich Institute of Biomedical Engineering, Technical University of Munich, Garching, 85748, Bavaria, Germany; Department of Diagnostic and Interventional Radiology, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, 81675, Bavaria, Germany.

Computers in biology and medicine

|December 20, 2024

概括

使用卷积神经网络 (CNN) 优化计算机断层扫描 (CT) 窗口设置,改善了慢性阻塞性肺病 (COPD) 检测. 手动调整肺瘤窗口获得了最高的准确性,优于自动化方法.

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
肺部医学肺部医学

背景情况:

慢性阻塞性肺病 (COPD) 诊断依赖于在肺部计算机断层扫描 (CT) 扫描中识别肺气.
图像窗口设置显著影响肺气的可见性,影响诊断的准确性.
卷积神经网络 (CNN) 在自动化医疗图像分析方面表现有前途.

研究的目的:

为了优化COPD的二进制检测基于使用CNN的肺气存在.
为了比较手动调整与自动窗口设置优化 (WSO) 在COPD检测CT图像上的有效性.
为此任务确定最有效的CNN架构.

主要方法:

追溯分析了78名受试者的7194张与对比度增强的CT图像 (3597名COPD,3597名对照).
探索手动剪切的肺瘤窗口设置和全范围窗口设置.
通过将定制层集成到CNN模型中来实现自动化的WSO,包括DenseNet.
使用接收器操作特征曲线 (AUC) 下面的面积来评估模型性能.

主要成果:

丹森网被确定为CNN最有效的骨干,在没有WSO的情况下实现了0.80的平均AUC.
手动将CT图像调整为肺瘤窗口设置,改善了DenseNet的COPD检测,平均AUC为0.86.
自动化WSO与定制的DenseNet层实现了0.82的平均AUC,学习最佳窗口设置在气瘤范围附近.

结论:

手动窗口设置优化到肺瘤范围显著提高了COPD检测准确度与DenseNet CNN模型.
自动化WSO提供了一个可行的替代方案,尽管手动优化目前提供了更高的性能.
窗口设置优化对于从CT数据中改进基于CNN的COPD检测至关重要.

关键词:

在美国,CNN是CNN.图像图像图像图像图像突发性瘤是什么意思优化优化优化优化窗口设置窗口设置

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