Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT

Gamechi, Zahra Sedghi; Arias-Lorza, Andres M.; Pedersen, Jesper Holst; Bruijne, Marleen de

doi:10.1117/12.2293748

Cited by 6 publications

(15 citation statements)

References 15 publications

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“…The existing classical method related to non-contrast CTs focus on the research of aortic calcification, 31 thoracic aorta territory, 32 and pulmonary artery. 33 But few studies involve the AAA. Our approach specifically concentrates on the segmentation task for abdominal aorta as well as aneurysm based on non-contrast-enhanced CTs.…”

Section: Conventional Methods Of Segmentation Of Aortamentioning

confidence: 99%

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Deep-learning approach to automate the segmentation of aorta in non-contrast CTs

Lucas

et al. 2023

J. Med. Imag.

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.PurposeSegmentation of vascular structures in preoperative computed tomography (CT) is a preliminary step for computer-assisted endovascular navigation. It is a challenging issue when contrast medium enhancement is reduced or impossible, as in the case of endovascular abdominal aneurysm repair for patients with severe renal impairment. In non-contrast–enhanced CTs, the segmentation tasks are currently hampered by the problems of low contrast, similar topological form, and size imbalance. To tackle these problems, we propose a novel fully automatic approach based on convolutional neural network.ApproachThe proposed method is implemented by fusing the features from different dimensions by three kinds of mechanisms, i.e., channel concatenation, dense connection, and spatial interpolation. The fusion mechanisms are regarded as the enhancement of features in non-contrast CTs where the boundary of aorta is ambiguous.ResultsAll of the networks are validated by three-fold cross-validation on our dataset of non-contrast CTs, which contains 5749 slices in total from 30 individual patients. Our methods achieve a Dice score of 88.7% as the overall performance, which is better than the results reported in the related works.ConclusionsThe analysis indicates that our methods yield a competitive performance by overcoming the above-mentioned problems in most general cases. Further, experiments on our non-contrast CTs demonstrate the superiority of the proposed methods, especially in low-contrast, similar-shaped, and extreme-sized cases.

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Section: Conventional Methods Of Segmentation Of Aortamentioning

confidence: 99%

“…Even though the inner lumen and the thrombus are not identified individually, it still makes sense for the guidance of EVAR. The existing classical method related to non-contrast CTs focus on the research of aortic calcification, 31 thoracic aorta territory, 32 and pulmonary artery 33 . But few studies involve the AAA.…”

Section: Related Workmentioning

confidence: 99%

Deep-learning approach to automate the segmentation of aorta in non-contrast CTs

Lucas

et al. 2023

J. Med. Imag.

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“…[3][4][5][6][7] Several methods for automated segmentation of the aorta and pulmonary artery from non-contrast-enhanced CT (NCE-CT) images have been proposed. [8][9][10][11][12][13][14][15] Isgum et al 8 proposed multi-atlas-based segmentation of the cardiac area and aorta in low-dose NCE-CT images. Kurugol et al 9 proposed an automated aorta segmentation and aortic calcification detection method using the aorta circular Hough transformation and refinement using three-dimensional (3D) level sets.…”

Section: Introductionmentioning

confidence: 99%

Aorta and main pulmonary artery segmentation using stacked U‐Net and localization on non‐contrast‐enhanced computed tomography images

et al. 2023

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BackgroundThe 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.PurposeA 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.MethodsNormal‐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.ResultsFor 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).ConclusionsSegmentation 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.

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“…While this approach involves minimal radiocontrast exposure, it poses challenges in differentiating blood pool regions and surrounding tissues. Prior studies developed an automatic segmentation method to overcome the difficulty in identifying Ao; however, similar analyses have not been conducted on PA ( 13 , 14 ). Our team previously developed a novel automated 3D segmentation method for both Ao and PA on non-contrast CT images ( 15 ).…”

Section: Introductionmentioning

confidence: 99%

Automated 3D segmentation of the aorta and pulmonary artery for predicting outcomes after thoracoscopic lobectomy in lung cancer patients

Lee

Chung²,

Wang³

et al. 2022

Front. Oncol.

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BackgroundPreoperative two-dimensional manual measurement of pulmonary artery diameter in a single-cut axial view computed tomography (CT) image is a commonly used non-invasive prediction method for pulmonary hypertension. However, the accuracy may be unreliable. Thus, this study aimed to evaluate the correlation of short-term surgical outcomes and pulmonary artery/aorta (PA/Ao) diameter ratio measured by automated three-dimensional (3D) segmentation in lung cancer patients who underwent thoracoscopic lobectomy.Materials and methodsWe included 383 consecutive lung cancer patients with thin-slice CT images who underwent lobectomy at a single institute between January 1, 2011 and December 31, 2019. Automated 3D segmentation models were used for 3D vascular reconstruction and measurement of the average diameters of Ao and PA. Propensity-score matching incorporating age, Charlson comorbidity index, and lobectomy performed by uniportal VATS was used to compare clinical outcomes in patients with PA/Ao ratio ≥1 and those <1.ResultsOur segmentation method measured 29 (7.57%) patients with a PA/Ao ratio ≥1. After propensity-score matching, a higher overall postoperative complication classified by the Clavien–Dindo classification (p = 0.016) were noted in patients with 3D PA/Ao diameter ratio ≥1 than those of <1. By multivariate logistic regression, patients with a 3D PA/Ao ratio ≥ 1 (p = 0.013) and tumor diameter > 3 cm (p = 0.002) both significantly predict the incidence of postoperative complications.ConclusionsPulmonary artery/aorta diameter ratio ≥ 1 measured by automated 3D segmentation may predict postoperative complications in lung cancer patients who underwent lobectomy.

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Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT

Cited by 6 publications

References 15 publications

Deep-learning approach to automate the segmentation of aorta in non-contrast CTs

Deep-learning approach to automate the segmentation of aorta in non-contrast CTs

Aorta and main pulmonary artery segmentation using stacked U‐Net and localization on non‐contrast‐enhanced computed tomography images

Automated 3D segmentation of the aorta and pulmonary artery for predicting outcomes after thoracoscopic lobectomy in lung cancer patients

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