Abstract：
UNASSIGNED: Chest X-rays (CXR) are widely used to facilitate the diagnosis and treatment of critically ill and emergency patients in clinical practice. Accurate hemi-diaphragm detection based on postero-anterior (P-A) CXR images is crucial for the diaphragm function assessment of critically ill and emergency patients to provide precision healthcare for these vulnerable populations.
UNASSIGNED: Therefore, an effective and accurate hemi-diaphragm detection method for P-A CXR images is urgently developed to assess these vulnerable populations\' diaphragm function.
UNASSIGNED: Based on the above, this paper proposes an effective hemi-diaphragm detection method for P-A CXR images based on the convolutional neural network (CNN) and graphics. First, we develop a robust and standard CNN model of pathological lungs trained by human P-A CXR images of normal and abnormal cases with multiple lung diseases to extract lung fields from P-A CXR images. Second, we propose a novel localization method of the cardiophrenic angle based on the two-dimensional projection morphology of the left and right lungs by graphics for detecting the hemi-diaphragm.
UNASSIGNED: The mean errors of the four key hemi-diaphragm points in the lung field mask images abstracted from static P-A CXR images based on five different segmentation models are 9.05, 7.19, 7.92, 7.27, and 6.73 pixels, respectively. Besides, the results also show that the mean errors of these four key hemi-diaphragm points in the lung field mask images abstracted from dynamic P-A CXR images based on these segmentation models are 5.50, 7.07, 4.43, 4.74, and 6.24 pixels,respectively.
UNASSIGNED: Our proposed hemi-diaphragm detection method can effectively perform hemi-diaphragm detection and may become an effective tool to assess these vulnerable populations\' diaphragm function for precision healthcare.
UNASSIGNED: Therefore, an effective and accurate hemi-diaphragm detection method for P-A CXR images is urgently developed to assess these vulnerable populations\' diaphragm function.
UNASSIGNED: Based on the above, this paper proposes an effective hemi-diaphragm detection method for P-A CXR images based on the convolutional neural network (CNN) and graphics. First, we develop a robust and standard CNN model of pathological lungs trained by human P-A CXR images of normal and abnormal cases with multiple lung diseases to extract lung fields from P-A CXR images. Second, we propose a novel localization method of the cardiophrenic angle based on the two-dimensional projection morphology of the left and right lungs by graphics for detecting the hemi-diaphragm.
UNASSIGNED: The mean errors of the four key hemi-diaphragm points in the lung field mask images abstracted from static P-A CXR images based on five different segmentation models are 9.05, 7.19, 7.92, 7.27, and 6.73 pixels, respectively. Besides, the results also show that the mean errors of these four key hemi-diaphragm points in the lung field mask images abstracted from dynamic P-A CXR images based on these segmentation models are 5.50, 7.07, 4.43, 4.74, and 6.24 pixels,respectively.
UNASSIGNED: Our proposed hemi-diaphragm detection method can effectively perform hemi-diaphragm detection and may become an effective tool to assess these vulnerable populations\' diaphragm function for precision healthcare.
摘要：
■胸部X射线（CXR）在临床实践中被广泛用于促进重症和急诊患者的诊断和治疗。基于后前（P-A）CXR图像的准确半隔膜检测对于危重和急诊患者的隔膜功能评估至关重要，从而为这些脆弱人群提供精确的医疗保健。
■因此，迫切需要一种有效且准确的P-ACXR图像半隔膜检测方法来评估这些脆弱人群的隔膜功能。
■基于上述内容，提出了一种有效的基于卷积神经网络(CNN)和图形的P-ACXR图像半膜片检测方法。首先,我们开发了一种健壮的标准的病理肺CNN模型，该模型由患有多种肺部疾病的正常和异常病例的人类P-ACXR图像训练，以从P-ACXR图像中提取肺野。第二,我们提出了一种基于左右两个肺的二维投影形态的心膈角的定位方法，用于通过图形检测半膈。
■从基于五种不同分割模型的静态P-ACXR图像中提取的肺野掩模图像中四个关键半膈点的平均误差分别为9.05、7.19、7.92、7.27和6.73像素，分别。此外,结果还表明，基于这些分割模型从动态P-ACXR图像中提取的肺野掩模图像中这四个关键半隔膜点的平均误差分别为5.50、7.07、4.43、4.74和6.24像素，分别。
■我们提出的半隔膜检测方法可以有效地进行半隔膜检测，并可能成为评估这些脆弱人群\'隔膜功能的有效工具，以进行精准医疗。
■因此，迫切需要一种有效且准确的P-ACXR图像半隔膜检测方法来评估这些脆弱人群的隔膜功能。
■基于上述内容，提出了一种有效的基于卷积神经网络(CNN)和图形的P-ACXR图像半膜片检测方法。首先,我们开发了一种健壮的标准的病理肺CNN模型，该模型由患有多种肺部疾病的正常和异常病例的人类P-ACXR图像训练，以从P-ACXR图像中提取肺野。第二,我们提出了一种基于左右两个肺的二维投影形态的心膈角的定位方法，用于通过图形检测半膈。
■从基于五种不同分割模型的静态P-ACXR图像中提取的肺野掩模图像中四个关键半膈点的平均误差分别为9.05、7.19、7.92、7.27和6.73像素，分别。此外,结果还表明，基于这些分割模型从动态P-ACXR图像中提取的肺野掩模图像中这四个关键半隔膜点的平均误差分别为5.50、7.07、4.43、4.74和6.24像素，分别。
■我们提出的半隔膜检测方法可以有效地进行半隔膜检测，并可能成为评估这些脆弱人群\'隔膜功能的有效工具，以进行精准医疗。
