Segmentation of aorta and main pulmonary artery of non-contrast CT images using U-Net for chronic thromboembolic pulmonary hypertension: evaluation of robustness to contacts with blood vessels
Segmentation of aorta and main pulmonary artery of non-contrast CT images using U-Net for chronic thromboembolic pulmonary hypertension: evaluation of robustness to contacts with blood vessels
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DOI：
10.1117/12.2612705
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发表时间：
2022-04
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通讯作者：
H. Suzuki;M. Matsuhiro;Y. Kawata;T. Sugiura;N. Tanabe;M. Kusumoto;M. Kaneko;N. Niki
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作者：
H. Suzuki;M. Matsuhiro;Y. Kawata;T. Sugiura;N. Tanabe;M. Kusumoto;M. Kaneko;N. Niki
肺动脉狭窄是肺动脉高压患者的一种形态学异常。主动脉和主肺动脉（MPA）的直径可用于预测肺动脉高压的存在。从非对比CT图像自动分割主动脉和MPA的一个主要问题是与血管接触引起的不可见边界。在这项研究中，我们应用U-Net的分割主动脉和MPA从非造影CT图像的正常和慢性血栓栓塞性肺动脉高压（CTEPH）的情况下，并评估血管之间的接触的鲁棒性。我们的分割方法包括三个步骤：（1）检测气管分支点，（2）裁剪感兴趣区域的中心气管分支点，和（3）分割主动脉和MPA使用U-网络。比较了七种方法的分割性能：2D U-Net，2D U-Net与预训练的VGG-16编码器，2D U-Net与预训练的VGG-19编码器，2D Attention U-Net，3D U-Net，它们的集成方法，以及我们的传统方法。使用这些U-Net的主动脉和MPA分割方法实现了比传统方法更高的性能。与非接触边界相比，血管的接触边界导致较低的性能，而平均边界距离低于约一个像素。
Enlargement of the pulmonary artery is a morphological abnormality of pulmonary hypertension patients. Diameters of the aorta and main pulmonary artery (MPA) are useful for predicting the presence of pulmonary hypertension. A major problem in the automatic segmentation of the aorta and MPA from non-contrast CT images is the invisible boundary caused by contact with blood vessels. In this study, we applied U-Net to the segmentation of the aorta and MPA from non-contrast CT images for normal and chronic thromboembolic pulmonary hypertension (CTEPH) cases and evaluated the robustness to the contacts between blood vessels. Our approach of the segmentation consists of three steps: (1) detection of trachea branch point, (2) cropping region of interest centered to the trachea branch point, and (3) segmentation of the aorta and MPA using U-Net. The segmentation performances were compared in seven methods: 2D U-Net, 2D U-Net with pre-trained VGG-16 encoder, 2D U-Net with pre-trained VGG-19 encoder, 2D Attention U-Net, 3D U-Net, an ensemble method of them, and our conventional method. The aorta and MPA segmentation methods using these U-Net achieved higher performance than a conventional method. The contact boundaries of blood vessels caused lower performance compared with the non-contact boundaries, whereas the mean boundary distances were below about one pixel.