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Yang J, Li X, Cheng JZ, Xue Z, Shi F, Ji Y, Wang X, Yang F. Segment aorta and localize landmarks simultaneously on noncontrast CT using a multitask learning framework for patients without severe vascular disease. Comput Biol Med 2023; 160:107002. [PMID: 37187136 DOI: 10.1016/j.compbiomed.2023.107002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/29/2023] [Accepted: 05/02/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND Non-contrast chest CT is widely used for lung cancer screening, and its images carry potential information of the thoracic aorta. The morphological assessment of the thoracic aorta may have potential value in the presymptomatic detection of thoracic aortic-related diseases and the risk prediction of future adverse events. However, due to low vasculature contrast in such images, visual assessment of aortic morphology is challenging and highly depends on physicians' experience. PURPOSE The main objective of this study is to propose a novel multi-task framework based on deep learning for simultaneous aortic segmentation and localization of key landmarks on unenhanced chest CT. The secondary objective is to use the algorithm to measure quantitative features of thoracic aorta morphology. METHODS The proposed network is composed of two subnets to carry out segmentation and landmark detection, respectively. The segmentation subnet aims to demarcate the aortic sinuses of the Valsalva, aortic trunk and aortic branches, whereas the detection subnet is devised to locate five landmarks on the aorta to facilitate morphology measures. The networks share a common encoder and run decoders in parallel, taking full advantage of the synergy of the segmentation and landmark detection tasks. Furthermore, the volume of interest (VOI) module and the squeeze-and-excitation (SE) block with attention mechanisms are incorporated to further boost the capability of feature learning. RESULTS Benefiting from the multitask framework, we achieved a mean Dice score of 0.95, average symmetric surface distance of 0.53 mm, Hausdorff distance of 2.13 mm for aortic segmentation, and mean square error (MSE) of 3.23 mm for landmark localization in 40 testing cases. CONCLUSION We proposed a multitask learning framework which can perform segmentation of the thoracic aorta and localization of landmarks simultaneously and achieved good results. It can support quantitative measurement of aortic morphology for further analysis of aortic diseases, such as hypertension.
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Affiliation(s)
- Jinrong Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Xiang Li
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Jie-Zhi Cheng
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Zhong Xue
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Yuqing Ji
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China
| | - Xuechun Wang
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai, 201807, China.
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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Canals P, Balocco S, Díaz O, Li J, García-Tornel A, Tomasello A, Olivé-Gadea M, Ribó M. A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning. Comput Med Imaging Graph 2023; 104:102170. [PMID: 36634467 DOI: 10.1016/j.compmedimag.2022.102170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 11/14/2022] [Accepted: 12/24/2022] [Indexed: 12/29/2022]
Abstract
Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients.
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Affiliation(s)
- P Canals
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.
| | - S Balocco
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain; Computer Vision Center, Bellaterra, Spain
| | - O Díaz
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - J Li
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - A García-Tornel
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - A Tomasello
- Neuroradiology, Vall d'Hebron Hospital Universitari, Barcelona, Spain
| | - M Olivé-Gadea
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - M Ribó
- Stroke Unit, Neurology, Hospital Vall d'Hebron, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
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Riffaud S, Ravon G, Allard T, Bernard F, Iollo A, Caradu C. Automatic branch detection of the arterial system from abdominal aortic segmentation. Med Biol Eng Comput 2022; 60:2639-2654. [DOI: 10.1007/s11517-022-02603-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 06/07/2022] [Indexed: 11/30/2022]
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Automated ascending aorta delineation from ECG-gated computed tomography images. Med Biol Eng Comput 2022; 60:2095-2108. [DOI: 10.1007/s11517-022-02588-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 05/08/2022] [Indexed: 01/16/2023]
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Dux-Santoy L, Rodríguez-Palomares JF, Teixidó-Turà G, Ruiz-Muñoz A, Casas G, Valente F, Servato ML, Galian-Gay L, Gutiérrez L, González-Alujas T, Fernández-Galera R, Evangelista A, Ferreira-González I, Guala A. Registration-based semi-automatic assessment of aortic diameter growth rate from contrast-enhanced computed tomography outperforms manual quantification. Eur Radiol 2021; 32:1997-2009. [PMID: 34655311 DOI: 10.1007/s00330-021-08273-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/29/2021] [Accepted: 08/14/2021] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Manual assessment of aortic diameters on double-oblique reformatted computed tomography angiograms (CTA) is considered the current standard, although the reproducibility for growth rates has not been reported. Deformable registration of CTA has been proposed to provide 3D aortic diameters and growth maps, but validation is lacking. This study aimed to quantify accuracy and inter-observer reproducibility of registration-based and manual assessment of aortic diameters and growth rates. METHODS Forty patients with ≥ 2 CTA acquired at least 6 months apart were included. Aortic diameters and growth rate were obtained in the aortic root and the entire thoracic aorta using deformable image registration by two independent observers, and compared with the current standard at typical anatomical landmarks. RESULTS Compared with manual assessment, the registration-based technique presented low bias (0.46 mm), excellent agreement (ICC = 0.99), and similar inter-observer reproducibility (ICC = 0.99 for both) for aortic diameters; and low bias (0.10 mm/year), good agreement (ICC = 0.82), and much higher inter-observer reproducibility for growth rates (root: ICC = 0.96 vs 0.68; thoracic aorta: ICC = 0.96 vs 0.80). Registration-based growth rate reproducibility over a 6-month-long follow-up was similar to that obtained by manual assessment after 2.7 years (LoA = [- 0.01, 0.33] vs [- 0.13, 0.21] mm/year, respectively). Mapping of diameter and growth rate was highly reproducible (ICC > 0.9) in the whole thoracic aorta. CONCLUSIONS Registration-based assessment of aortic dilation on CTA is accurate and substantially more reproducible than the current standard, even at follow-up as short as 6 months, and provides robust 3D mapping of aortic diameters and growth rates beyond the pre-established anatomic landmarks. KEY POINTS • Registration-based semi-automatic assessment of progressive aortic dilation on CTA is accurate and substantially more reproducible than the current standard. • The registration-based technique allows robust growth rate assessment at follow-up as short as 6 months, with a similar reproducibility to that obtained by manual assessment at around 3 years. • The use of image registration provides robust 3D mapping of aortic diameters and growth rates beyond the pre-established anatomic landmarks.
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Affiliation(s)
| | - Jose F Rodríguez-Palomares
- Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain.
- CIBER-CV, Instituto de Salud Carlos III, Madrid, Spain.
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain.
- Universitat Autònoma de Barcelona, Bellaterra, Spain.
| | - Gisela Teixidó-Turà
- Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain
- CIBER-CV, Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Aroa Ruiz-Muñoz
- Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain
- CIBER-CV, Instituto de Salud Carlos III, Madrid, Spain
| | - Guillem Casas
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Filipa Valente
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Maria Luz Servato
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Laura Galian-Gay
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Laura Gutiérrez
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Teresa González-Alujas
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Rubén Fernández-Galera
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Arturo Evangelista
- Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain
- CIBER-CV, Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain
- Instituto del Corazón. Quirónsalud-Teknon, Barcelona, Spain
| | - Ignacio Ferreira-González
- Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain.
- Department of Cardiology, Hospital Universitari Vall D'Hebron, Paseo Vall d'Hebron 119-129, 08035, Barcelona, Spain.
- Universitat Autònoma de Barcelona, Bellaterra, Spain.
- CIBER de Epidemiología y Salud Pública, CIBERESP, Instituto de Salud Carlos III, Madrid, Spain.
| | - Andrea Guala
- Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain
- CIBER-CV, Instituto de Salud Carlos III, Madrid, Spain
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Cai W, Wang Y, Gu L, Ji X, Shen Q, Ren X. Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:2670793. [PMID: 34471506 PMCID: PMC8405334 DOI: 10.1155/2021/2670793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 08/08/2021] [Accepted: 08/12/2021] [Indexed: 11/17/2022]
Abstract
This paper presents an in-depth study and analysis of the 3D arterial centerline in spiral CT coronary angiography, and constructs its detection and extraction technique. The first time, the distance transform is used to complete the boundary search of the original figure; the second time, the distance transform is used to calculate the value of the distance transform of all voxels, and according to the value of the distance transform, unnecessary voxels are deleted, to complete the initial contraction of the vascular region and reduce the computational consumption in the next process; then, the nonwitnessed voxels are used to construct the maximum inner joint sphere model and find the skeletal voxels that can reflect the shape of the original figure. Finally, the skeletal lines were optimized on these initially extracted skeletal voxels using a dichotomous-like principle to obtain the final coronary artery centerline. Through the evaluation of the experimental results, the algorithm can extract the coronary centerline more accurately. In this paper, the segmentation method is evaluated on the test set data by two kinds of indexes: one is the index of segmentation result evaluation, including dice coefficient, accuracy, specificity, and sensitivity; the other is the index of clinical diagnosis result evaluation, which is to refine the segmentation result for vessel diameter detection. The results obtained in this paper were compared with the physicians' labeling results. In terms of network performance, the Dice coefficient obtained in this paper was 0.89, the accuracy was 98.36%, the sensitivity was 93.36%, and the specificity was 98.76%, which reflected certain advantages in comparison with the advanced methods proposed by previous authors. In terms of clinical evaluation indexes, by performing skeleton line extraction and diameter calculation on the results obtained by the segmentation method proposed in this paper, the absolute error obtained after comparing with the diameter of the labeled image was 0.382 and the relative error was 0.112, which indicates that the segmentation method in this paper can recover the vessel contour more accurately. Then, the results of coronary artery centerline extraction with and without fine branch elimination were evaluated, which proved that the coronary artery centerline has higher accuracy after fine branch elimination. The algorithm is also used to extract the centerline of the complete coronary artery tree, and the results prove that the algorithm has better results for the centerline extraction of the complete coronary vascular tree.
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Affiliation(s)
- Wenjuan Cai
- Changshu Hospital of Chinese Medicine, Changshu 215516, Jiangsu, China
| | - Yanzhe Wang
- Changshu Hospital of Chinese Medicine, Changshu 215516, Jiangsu, China
| | - Liya Gu
- Changshu Hospital of Chinese Medicine, Changshu 215516, Jiangsu, China
| | - Xuefeng Ji
- Changshu Hospital of Chinese Medicine, Changshu 215516, Jiangsu, China
| | - Qiusheng Shen
- Changshu Hospital of Chinese Medicine, Changshu 215516, Jiangsu, China
| | - Xiaogang Ren
- Changshu Hospital of Chinese Medicine, Changshu 215516, Jiangsu, China
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
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Deep learning method for aortic root detection. Comput Biol Med 2021; 135:104533. [PMID: 34139438 DOI: 10.1016/j.compbiomed.2021.104533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 05/25/2021] [Accepted: 05/25/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. This study demonstrates the feasibility of a fully automatic method for detecting the aortic root, which is a key anatomical landmark in this type of procedure. The approach is based on the use of deep learning techniques that attempt to mimic expert behavior. METHODS A total of 69 CTA scans (39 for training and 30 for validation) with different pathology types were selected to train the network. Furthermore, a total of 71 CTA scans were selected independently and applied as the test set to assess their performance. RESULTS The accuracy was evaluated by comparing the locations marked by the method with benchmark locations (which were manually marked by two experts). The interobserver error was 4.6 ± 2.3 mm. On an average, the differences between the locations marked by the two experts and those detected by the computer were 6.6 ± 3.0 mm and 6.8 ± 3.3 mm, respectively, when calculated using the test set. CONCLUSIONS From an analysis of these results, we can conclude that the proposed method based on pre-trained CNN models can accurately detect the aortic root in CTA images without prior segmentation.
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