The image quality of portable supine chest radiographs is inherently poor due
to low contrast and high noise. The endotracheal intubation detection requires
the locations of the endotracheal tube (ETT) tip and carina. The goal is to
find the distance between the ETT tip and the carina in chest radiography. To
overcome such a problem, we propose a feature extraction method with Mask
R-CNN. The Mask R-CNN predicts a tube and a tracheal bifurcation in an image.
Then, the feature extraction method is used to find the feature point of the
ETT tip and that of the carina. Therefore, the ETT-carina distance can be
obtained. In our experiments, our results can exceed 96\% in terms of recall
and precision. Moreover, the object error is less than $4.7751\pm 5.3420$ mm,
and the ETT-carina distance errors are less than $5.5432\pm 6.3100$ mm. The
external validation shows that the proposed method is a high-robustness system.
According to the Pearson correlation coefficient, we have a strong correlation
between the board-certified intensivists and our result in terms of ETT-carina
distance.

本文研究了应用 Mask R-CNN 作为特征提取方法来解决可移动卧位胸部 X 射线片在检测气管插管和检测位置时所面临的问题，通过该方法可以提取出气管插管以及在图像中的叉状气管。实验结果证明了该方法的高鲁棒性，对于 ETT-carina 距离的检测精度达到了 5mm 左右。