鈴木, 秀宣 Tokushima University 徳島大学 教育研究者総覧 KAKEN研究者をさがす
松廣, 幹雄 Tokushima University KAKEN研究者をさがす
河田, 佳樹 Tokushima University 徳島大学 教育研究者総覧 KAKEN研究者をさがす
Sugiura, Toshihiko Chiba University
Tanabe, Nobuhiro Chiba University
Kusumoto, Masahiko National Cancer Center
Kaneko, Masahiro Tokyo Health Service Association
仁木, 登 Medical Science Institute 徳島大学 教育研究者総覧 KAKEN研究者をさがす
Chronic thromboembolic pulmonary hypertension
contact surface ratio
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. Although the contact boundaries of blood vessels caused lower performance compared with the non-contact boundaries, the mean boundary distances were below about one pixel.
Proceedings of SPIE
Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Toshihiko Sugiura, Nobuhiro Tanabe, Masahiko Kusumoto, Masahiro Kaneko, and Noboru Niki "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", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203325 (4 April 2022); https://doi.org/10.1117/12.2612705
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