Aorta and main pulmonary artery segmentation using stacked U‐Net and localization on non‐contrast‐enhanced computed tomography images - Suzuki, Hidenobu; Kawata, Yoshiki; Aokage, Keiju; Matsumoto, Yuji; Sugiura, Toshihiko; Tanabe, Nobuhiro; Nakano, Yasutaka; Tsuchida, Takaaki; Kusumoto, Masahiko; Marumo, Kazuyoshi; Kaneko, Masahiro; Niki, Noboru (Medical Physics, 2024)
Background The contact between the aorta, main pulmonary artery (MPA), main pulmonary vein, vena cava (VC), and esophagus affects segmentation of the aorta and MPA in non‐contrast‐enhanced computed tomography (NCE‐CT) images. Purpose A two‐stage stacked U‐Net and localization of the aorta and MPA were developed for the segmentation of the aorta and MPA in NCE‐CT images. Methods Normal‐dose NCE‐CT images of 24 subjects with chronic thromboembolic pulmonary hypertension (CTEPH) and low‐dose NCE‐CT images of 100 subjects without CTEPH were used in this study. The aorta is in contact with the ascending aorta (AA) and MPA, the AA with the VC, the aortic arch (AR) with the VC and esophagus, and the descending aorta (DA) with the esophagus. These contact surfaces were manually annotated. The contact surfaces were quantified using the contact surface ratio (CSR). Segmentation of the aorta and MPA in NCE‐CT images was performed by localization of the aorta and MPA and a two‐stage stacked U‐Net. Localization was performed by extracting and processing the trachea and main bronchus. The first stage of the stacked U‐Net consisted of a 2D U‐Net, 2D U‐Net with a pre‐trained VGG‐16 encoder, and 2D attention U‐Net. The second stage consisted of a 3D U‐Net with four input channels: the CT volume and three segmentation results of the first stage. The model was trained and tested using 10‐fold cross‐validation. Segmentation of the entire volume was evaluated using the Dice similarity coefficient (DSC). Segmentation of the contact area was also assessed using the mean surface distance (MSD). The statistical analysis of the evaluation underwent a multi‐comparison correction. CTEPH and non‐CTEPH cases were classified based on the vessel diameters measured from the segmented MPA. Results For the noncontact surfaces of AA, the MSD of stacked U‐Net was 0.31 ± 0.10 mm (p < 0.05) and 0.32 ± 0.13 mm (p < 0.05) for non‐CTEPH and CTEPH cases, respectively. For contact surfaces with a CSR of 0.4 or greater in AA, the MSD was 0.52 ± 0.23 mm (p < 0.05), and 0.68 ± 0.29 mm (p > 0.05) for non‐CTEPH and CTEPH cases, respectively. MSDs were lower than those of 2D and 3D U‐Nets for contact and noncontact surfaces; moreover, MSDs increased slightly with larger CSRs. However, the stacked U‐Net achieved MSDs of approximately 1 pixel for a wide contact surface. The area under the receiver operating characteristic curve for CTEPH and non‐CTEPH classification using the right main pulmonary artery (RMPA) diameter was 0.97 (95% confidence interval [CI]: 0.94–1.00). Conclusions Segmentation of the aorta and MPA on NCE‐CT images were affected by vascular and esophageal contact. The application of stacked U‐Net and localization techniques for non‐CTEPH and CTEPH cases mitigated the impact of contact, suggesting its potential for diagnosing CTEPH.