基于从胸部 X 光图像中提取的肺野自动计算心胸比例,无需进行心脏分割

Yingjian Yang, Jie Zheng, Peng Guo, Tianqi Wu, Qi Gao, Yingwei Guo, Ziran Chen, Chengcheng Liu, Zhanglei Ouyang, Huai Chen, Yan Kang
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摘要

基于后正位胸部 X 光(P-A CXR)图像的心胸比率(CTR)是最常用的心脏测量方法之一,也是初步评估心脏疾病的指标。然而,与肺野相比,心脏在 P-A CXR 图像上不易观察到。因此,放射科医生通常会根据 P-A CXR 图像手动确定与心脏相邻的左右肺野的左右心脏边界点。基于上述情况,本文提出了一种新颖的全自动 CTR 计算方法,该方法基于利用卷积神经网络(CNN)从 P-A CXR 图像中抽象出的肺野,克服了心脏分割的局限性,避免了心脏分割的误差。首先,根据预先训练的卷积神经网络从 P-A CXR 图像中抽象出肺野掩模图像。结果表明,在测试集 T1(21 × 512 × 512 静态 P-A CXR 图像)和 T2(13 × 512 × 512 动态 P-A CXR 图像)中,基于各种预训练 CNN 的 CTR 四个关键点在 x 轴方向上的平均距离误差分别为 4.1161 和 3.2116 像素。此外,在测试集 T1 和 T2 上,基于四种提议模型的平均 CTR 误差分别为 0.0208 和 0.0180。我们提议的模型实现了与之前 CardioNet 模型相当的 CTR 计算性能,克服了心脏分割问题,并且耗时更短。因此,我们提出的方法切实可行,可以成为初步评估心脏疾病的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic cardiothoracic ratio calculation based on lung fields abstracted from chest X-ray images without heart segmentation
The cardiothoracic ratio (CTR) based on postero-anterior chest X-rays (P-A CXR) images is one of the most commonly used cardiac measurement methods and an indicator for initially evaluating cardiac diseases. However, the hearts are not readily observable on P-A CXR images compared to the lung fields. Therefore, radiologists often manually determine the CTR’s right and left heart border points of the adjacent left and right lung fields to the heart based on P-A CXR images. Meanwhile, manual CTR measurement based on the P-A CXR image requires experienced radiologists and is time-consuming and laborious.Based on the above, this article proposes a novel, fully automatic CTR calculation method based on lung fields abstracted from the P-A CXR images using convolutional neural networks (CNNs), overcoming the limitations to heart segmentation and avoiding errors in heart segmentation. First, the lung field mask images are abstracted from the P-A CXR images based on the pre-trained CNNs. Second, a novel localization method of the heart’s right and left border points is proposed based on the two-dimensional projection morphology of the lung field mask images using graphics.The results show that the mean distance errors at the x-axis direction of the CTR’s four key points in the test sets T1 (21 × 512 × 512 static P-A CXR images) and T2 (13 × 512 × 512 dynamic P-A CXR images) based on various pre-trained CNNs are 4.1161 and 3.2116 pixels, respectively. In addition, the mean CTR errors on the test sets T1 and T2 based on four proposed models are 0.0208 and 0.0180, respectively.Our proposed model achieves the equivalent performance of CTR calculation as the previous CardioNet model, overcomes heart segmentation, and takes less time. Therefore, our proposed method is practical and feasible and may become an effective tool for initially evaluating cardiac diseases.
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