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Jiang M, Chiu B. A Dual-Stream Centerline-Guided Network for Segmentation of the Common and Internal Carotid Arteries From 3D Ultrasound Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2690-2705. [PMID: 37015114 DOI: 10.1109/tmi.2023.3263537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Segmentation of the carotid section encompassing the common carotid artery (CCA), the bifurcation and the internal carotid artery (ICA) from three-dimensional ultrasound (3DUS) is required to measure the vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT), shown to be sensitive to treatment effect. We proposed an approach to combine a centerline extraction network (CHG-Net) and a dual-stream centerline-guided network (DSCG-Net) to segment the lumen-intima (LIB) and media-adventitia boundaries (MAB) from 3DUS images. Correct arterial location is essential for successful segmentation of the carotid section encompassing the bifurcation. We addressed this challenge by using the arterial centerline to enhance the localization accuracy of the segmentation network. The CHG-Net was developed to generate a heatmap indicating high probability regions for the centerline location, which was then integrated with the 3DUS image by the DSCG-Net to generate the MAB and LIB. The DSCG-Net includes a scale-based and a spatial attention mechanism to fuse multi-level features extracted by the encoder, and a centerline heatmap reconstruction side-branch connected to the end of the encoder to increase the generalization ability of the network. Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.8±1.9% and 92.3±5.4% for CCA MAB and LIB, respectively, and 93.2±4.4% and 89.0±10.0% for ICA MAB and LIB, respectively. Our approach outperformed four state-of-the-art 3D CNN models, even after their performances were boosted by centerline guidance. The efficiency afforded by the framework would allow it to be incorporated into the clinical workflow for improved quantification of plaque change.
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Zhou H, Xiao J, Ganesh S, Lerner A, Ruan D, Fan Z. VWI-APP: Vessel wall imaging-dedicated automated processing pipeline for intracranial atherosclerotic plaque quantification. Med Phys 2023; 50:1496-1506. [PMID: 36345580 PMCID: PMC10033308 DOI: 10.1002/mp.16074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/16/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Quantitative plaque assessment based on 3D magnetic resonance (MR) vessel wall imaging (VWI) has been shown to provide valuable numerical markers of the burden and risk of intracranial atherosclerotic disease (ICAD). However, plaque quantification is currently time-consuming and observer-dependent due to the demand for heavy manual effort. A VWI-dedicated automated processing pipeline (VWI-APP) is desirable. PURPOSE To develop and evaluate a VWI-APP for end-to-end quantitative analysis of intracranial atherosclerotic plaque. METHODS We retrospectively enrolled 91 subjects with ICAD (80 for pipeline development, 10 for an end-to-end pipeline evaluation, and 1 for demonstrating longitudinal plaque assessment) who had undergone VWI and MR angiography. In an end-to-end evaluation, diameter stenosis (DS), normalized wall index (NWI), remodeling ratio (RR), plaque wall contrast ratio (CR), and total plaque volume (TPV) were quantified at each culprit lesion using the developed VWI-APP and a computer-aided manual approach by a neuroradiologist, respectively. The time consumed in each quantification approach was recorded. Two-sided paired t-tests and intraclass correlation coefficient (ICC) were used to determine the difference and agreement in each plaque metric between VWI-APP and manual quantification approaches. RESULTS There was no significant difference between VWI-APP and manual quantification in each plaque metric. The ICC was 0.890, 0.813, 0.827, 0.891, and 0.991 for DS, NWI, RR, CR, and TPV, respectively, suggesting good to excellent accuracy of the pipeline method in plaque quantification. Quantitative analysis of each culprit lesion on average took 675.7 s using the manual approach but shortened to 238.3 s with the aid of VWI-APP. CONCLUSIONS VWI-APP is an accurate and efficient approach to intracranial atherosclerotic plaque quantification. Further clinical assessment of this automated tool is warranted to establish its utility in the risk assessment of ICAD lesions.
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Affiliation(s)
- Hanyue Zhou
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jiayu Xiao
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA
| | - Siddarth Ganesh
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Alexander Lerner
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA
| | - Dan Ruan
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA
| | - Zhaoyang Fan
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Radiation Oncology, University of Southern California, Los Angeles, CA 90033, USA
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Huang X, Wang J, Li Z. 3D carotid artery segmentation using shape-constrained active contours. Comput Biol Med 2023; 153:106530. [PMID: 36610215 DOI: 10.1016/j.compbiomed.2022.106530] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 12/12/2022] [Accepted: 12/31/2022] [Indexed: 01/04/2023]
Abstract
Reconstruction of the carotid artery is demanded in the detection and characterization of atherosclerosis. This study proposes a shape-constrained active contour model for segmenting the carotid artery from MR images, which embeds the output of the deep learning network into the active contour. First the centerline of the carotid artery is localized and then modified active contour initialized from the centerline is used to extract the vessel lumen, finally the probability atlas generated by the deep learning network in polar representation domain is integrated into the active contour as a prior information to detect the outer wall. The results showed that the proposed active contour model was efficient and comparable to manual segmentation.
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Affiliation(s)
- Xianjue Huang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jun Wang
- First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Zhiyong Li
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China; School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, 4000, Australia; Faculty of Sports Science, Ningbo University, Ningbo, 315211, China.
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Sedghi Gamechi Z, Arias-Lorza AM, Saghir Z, Bos D, de Bruijne M. Assessment of fully automatic segmentation of pulmonary artery and aorta on noncontrast CT with optimal surface graph cuts. Med Phys 2021; 48:7837-7849. [PMID: 34653274 PMCID: PMC9298252 DOI: 10.1002/mp.15289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 08/24/2021] [Accepted: 09/09/2021] [Indexed: 01/29/2023] Open
Abstract
Purpose Accurate segmentation of the pulmonary arteries and aorta is important due to the association of the diameter and the shape of these vessels with several cardiovascular diseases and with the risk of exacerbations and death in patients with chronic obstructive pulmonary disease. We propose a fully automatic method based on an optimal surface graph‐cut algorithm to quantify the full 3D shape and the diameters of the pulmonary arteries and aorta in noncontrast computed tomography (CT) scans. Methods The proposed algorithm first extracts seed points in the right and left pulmonary arteries, the pulmonary trunk, and the ascending and descending aorta by using multi‐atlas registration. Subsequently, the centerlines of the pulmonary arteries and aorta are extracted by a minimum cost path tracking between the extracted seed points, with a cost based on a combination of lumen intensity similarity and multiscale medialness in three planes. The centerlines are refined by applying the path tracking algorithm to curved multiplanar reformatted scans and are then smoothed and dilated nonuniformly according to the extracted local vessel radius from the medialness filter. The resulting coarse estimates of the vessels are used as initialization for a graph‐cut segmentation. Once the vessels are segmented, the diameters of the pulmonary artery (PA) and the ascending aorta (AA) and the PA:AA ratio are automatically calculated both in a single axial slice and in a 10 mm volume around the automatically extracted PA bifurcation level. The method is evaluated on noncontrast CT scans from the Danish Lung Cancer Screening Trial (DLCST). Segmentation accuracy is determined by comparing with manual annotations on 25 CT scans. Intraclass correlation (ICC) between manual and automatic diameters, both measured in axial slices at the PA bifurcation level, is computed on an additional 200 CT scans. Repeatability of the automated 3D volumetric diameter and PA:AA ratio calculations (perpendicular to the vessel axis) are evaluated on 118 scan–rescan pairs with an average in‐between time of 3 months. Results We obtained a Dice segmentation overlap of 0.94 ± 0.02 for pulmonary arteries and 0.96 ± 0.01 for the aorta, with a mean surface distance of 0.62 ± 0.33 mm and 0.43 ± 0.07 mm, respectively. ICC between manual and automatic in‐slice diameter measures was 0.92 for PA, 0.97 for AA, and 0.90 for the PA:AA ratio, and for automatic diameters in 3D volumes around the PA bifurcation level between scan and rescan was 0.89, 0.95, and 0.86, respectively. Conclusion The proposed automatic segmentation method can reliably extract diameters of the large arteries in non‐ECG‐gated noncontrast CT scans such as are acquired in lung cancer screening.
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Affiliation(s)
- Zahra Sedghi Gamechi
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Andres M Arias-Lorza
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Zaigham Saghir
- Department of Respiratory Medicine, Gentofte University Hospital, Hellerup, Denmark
| | - Daniel Bos
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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Ziegler M, Alfraeus J, Bustamante M, Good E, Engvall J, de Muinck E, Dyverfeldt P. Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic. BMC Med Imaging 2021; 21:38. [PMID: 33639893 PMCID: PMC7912466 DOI: 10.1186/s12880-021-00568-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/15/2021] [Indexed: 11/24/2022] Open
Abstract
Background Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. The purpose of this study was to validate and demonstrate a method for segmenting the carotid bifurcation into the common, internal, and external carotid arteries (CCA, ICA, ECA) in contrast-enhanced MR angiography (CE-MRA) data. Methods A segmentation pipeline utilizing a convolutional neural network (DeepMedic) was tailored and trained for multi-class segmentation of the carotid arteries in CE-MRA data from the Swedish CardioPulmonsary bioImage Study (SCAPIS). Segmentation quality was quantitatively assessed using the Dice similarity coefficient (DSC), Matthews Correlation Coefficient (MCC), F2, F0.5, and True Positive Ratio (TPR). Segmentations were also assessed qualitatively, by three observers using visual inspection. Finally, geometric descriptions of the carotid bifurcations were generated for each subject to demonstrate the utility of the proposed segmentation method. Results Branch-level segmentations scored DSC = 0.80 ± 0.13, MCC = 0.80 ± 0.12, F2 = 0.82 ± 0.14, F0.5 = 0.78 ± 0.13, and TPR = 0.84 ± 0.16, on average in a testing cohort of 46 carotid bifurcations. Qualitatively, 61% of segmentations were judged to be usable for analyses without adjustments in a cohort of 336 carotid bifurcations without ground-truth. Carotid artery geometry showed wide variation within the whole cohort, with CCA diameter 8.6 ± 1.1 mm, ICA 7.5 ± 1.4 mm, ECA 5.7 ± 1.0 mm and bifurcation angle 41 ± 21°. Conclusion The proposed segmentation method automatically generates branch-level segmentations of the carotid arteries that are suitable for use in further analyses and help enable large-cohort investigations.
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Affiliation(s)
- Magnus Ziegler
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden. .,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - Jesper Alfraeus
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Mariana Bustamante
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Elin Good
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Cardiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Jan Engvall
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Clinical Physiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Ebo de Muinck
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Cardiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Petter Dyverfeldt
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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Zhu C, Wang X, Teng Z, Chen S, Huang X, Xia M, Mao L, Bai C. Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images. Phys Med Biol 2021; 66:045033. [PMID: 33333499 DOI: 10.1088/1361-6560/abd4bb] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Accurate and automatic carotid artery segmentation for magnetic resonance (MR) images is eagerly expected, which can greatly assist a comprehensive study of atherosclerosis and accelerate the translation. Although many efforts have been made, identification of the inner lumen and outer wall in diseased vessels is still a challenging task due to complex vascular deformation, blurred wall boundary, and confusing componential expression. In this paper, we introduce a novel fully automatic 3D framework for simultaneously segmenting the carotid artery from high-resolution multi-contrast MR sequences based on deep learning. First, an optimal channel fitting structure is designed for identity mapping, and a novel 3D residual U-net is used as a basic network. Second, high-resolution MR images are trained using both patch-level and global-level strategies, and the two pre-segmentation results are optimized based on structural characteristics. Third, the optimized pre-segmentation results are cascaded with the patch-cropped MR volume data and trained to segment the carotid lumen and wall. Extensive experiments demonstrate the proposed method outperforms the state-of-the-art 3D Unet-based segmentation models.
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Affiliation(s)
- Chenglu Zhu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
| | - Xiaoyan Wang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
| | - Zhongzhao Teng
- University Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom
| | - Shengyong Chen
- Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Xiaojie Huang
- The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, People's Republic of China
| | - Ming Xia
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
| | - Lizhao Mao
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
| | - Cong Bai
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
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Shauly O, Joskowicz L, Istoyler E, Nadler C. Parotid salivary ductal system segmentation and modeling in Sialo-CBCT scans. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2020.1866670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- O. Shauly
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - L. Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - E.G. Istoyler
- Oro-Maxillofacial Imaging, Oral Medicine Department, Hadassah School of Dental Medicine, the Hebrew of University, Jerusalem, Israel
| | - C. Nadler
- Oro-Maxillofacial Imaging, Oral Medicine Department, Hadassah School of Dental Medicine, the Hebrew of University, Jerusalem, Israel
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Levilly S, Castagna M, Idier J, Bonnefoy F, Le Touzé D, Moussaoui S, Paul-Gilloteaux P, Serfaty JM. Towards quantitative evaluation of wall shear stress from 4D flow imaging. Magn Reson Imaging 2020; 74:232-243. [PMID: 32889090 DOI: 10.1016/j.mri.2020.08.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 06/12/2020] [Accepted: 08/23/2020] [Indexed: 11/25/2022]
Abstract
Wall shear stress (WSS) is a relevant hemodynamic indicator of the local stress applied on the endothelium surface. More specifically, its spatiotemporal distribution reveals crucial in the evolution of many pathologies such as aneurysm, stenosis, and atherosclerosis. This paper introduces a new solution, called PaLMA, to quantify the WSS from 4D Flow MRI data. It relies on a two-step local parametric model, to accurately describe the vessel wall and the velocity-vector field in the neighborhood of a given point of interest. Extensive validations have been performed on synthetic 4D Flow MRI data, including four datasets generated from patient specific computational fluid dynamics simulations on carotids. The validation tests are focused on the impact of the noise component, of the resolution level, and of the segmentation accuracy concerning the vessel position in the context of complex flow patterns. In simulated cases aimed to reproduce clinical acquisition conditions, the WSS quantification performance reached by PaLMA is significantly higher (with a gain in RMSE of 12 to 27%) than the reference one obtained using the smoothing B-spline method proposed by Potters et al. (2015) method, while the computation time is equivalent for both WSS quantification methods.
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Affiliation(s)
- Sébastien Levilly
- Laboratoire des Sciences du Numérique de Nantes (ECN and CNRS), 1 rue de la Noë, BP 92101, 44321 Nantes Cedex 3, France.
| | - Marco Castagna
- Ecole Centrale de Nantes, LHEEA Lab (ECN and CNRS), 1 rue de la Noë, 44300 Nantes, France; Université de Nantes, CHU Nantes, CNRS UMR 6291, INSERM UMR 1087, L'institut du thorax, F-44000 Nantes, France
| | - Jérôme Idier
- Laboratoire des Sciences du Numérique de Nantes (ECN and CNRS), 1 rue de la Noë, BP 92101, 44321 Nantes Cedex 3, France
| | - Félicien Bonnefoy
- Ecole Centrale de Nantes, LHEEA Lab (ECN and CNRS), 1 rue de la Noë, 44300 Nantes, France
| | - David Le Touzé
- Ecole Centrale de Nantes, LHEEA Lab (ECN and CNRS), 1 rue de la Noë, 44300 Nantes, France
| | - Saïd Moussaoui
- Laboratoire des Sciences du Numérique de Nantes (ECN and CNRS), 1 rue de la Noë, BP 92101, 44321 Nantes Cedex 3, France
| | - Perrine Paul-Gilloteaux
- Université de Nantes, CHU Nantes, CNRS UMR 6291, INSERM UMR 1087, L'institut du thorax, F-44000 Nantes, France; Université de Nantes, CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UMS 3556, F-44000 Nantes, France
| | - Jean-Michel Serfaty
- Université de Nantes, CHU Nantes, CNRS UMR 6291, INSERM UMR 1087, L'institut du thorax, F-44000 Nantes, France
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Tsakanikas VD, Siogkas PK, Mantzaris MD, Potsika VT, Kigka VI, Exarchos TP, Koncar IB, Jovanovic M, Vujcic A, Ducic S, Pelisek J, Fotiadis DI. A deep learning oriented method for automated 3D reconstruction of carotid arterial trees from MR imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2408-2411. [PMID: 33018492 DOI: 10.1109/embc44109.2020.9176532] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can provide more accurate image segmentation of the carotid arterial tree and plaque tissue can help improve early diagnosis, prevention and treatment of carotid disease. Herein, we present a novel methodology combining the U-net model and morphological active contours in an iterative framework that accurately segments the carotid lumen and outer wall. The method automatically produces a 3D meshed model of the carotid bifurcation and smaller branches, using multispectral MR image series obtained from two clinical centres of the TAXINOMISIS study. As indicated by a validation study, the algorithm succeeds high accuracy (99.1% for lumen area and 92.6% for the perimeter) for lumen segmentation. The proposed algorithm will be used in the TAXINOMISIS study to obtain more accurate 3D vessel models for improved computational fluid dynamics simulations and the development of models of atherosclerotic plaque progression.
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10
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Wu J, Xin J, Yang X, Sun J, Xu D, Zheng N, Yuan C. Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black-blood vessel wall MRI. Med Phys 2019; 46:5544-5561. [PMID: 31356693 DOI: 10.1002/mp.13739] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 06/14/2019] [Accepted: 07/11/2019] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Early detection of carotid atherosclerosis on the vessel wall (VW) magnetic resonance imaging (MRI) (VW-MRI) images can prevent the progression of cardiovascular disease. However, the manual inspection process of the VW-MRI images is cumbersome and has low reproducibility. Therefore in this paper, by using the convolutional neural networks (CNNs), we develop a deep morphology aided diagnosis (DeepMAD) network for automated segmentation of the VW of carotid artery and for automated diagnosis of the carotid atherosclerosis with the black-blood (BB) VW-MRI (i.e., the T1-weighted MRI) in a slice-by-slice manner. METHODS The proposed DeepMAD network consists of a segmentation subnetwork and a diagnosis subnetwork for performing the segmentation and diagnosis tasks on the BB-VW-MRI images, where the manual labeled lumen area, the manual labeled outer wall area and the manual labeled lesion Types based on the modified American Heart Association (AHA) criteria are used as the ground-truth. Specifically, a deep U-shape CNN with a weighted fusion layer is designed as the segmentation subnetwork, where the lumen area and the outer wall area can be simultaneously segmented under the supervision of the triple Dice loss to provide the vessel wall map as morphological information. Then, the image stream from the BB-VWMRI image and the morphology stream from the obtained vessel wall map are extracted from two deep CNNs and combined to obtain the diagnosis results of atherosclerosis in the diagnosis subnetwork. In addition, the triple input set is formed by three carotid regions of interest (ROIs) from three consecutive slices of the MRI sequence and input to the DeepMAD network, where the first and last slices used as additional adjacent slices to provide 2.5D spatial information along the carotid artery centerline for the intermediate slice, which is the target slice for segmentation and diagnosis in the study. RESULTS Compared to other existing methods, the DeepMAD network can achieve promising segmentation performances (0.9594 Dice for the lumen and 0.9657 Dice for the outer wall) and better diagnosis Accuracy of the carotid atherosclerosis (0.9503 AUC and 0.8916 Accuracy) in the test dataset (including invisible subjects) from same source as the training dataset. In addition, the trained DeepMAD model can be successfully transferred to another test dataset for segmentation and diagnosis tasks with remarkable performance (0.9475 Dice for the lumen and 0.9542 Dice for the outer wall, 0. 9227 AUC and 0.8679 Accuracy for diagnosis). CONCLUSIONS Even without the intervention of reviewers required for previous works, the proposed DeepMAD network automatically segments the lumen and the outer wall together and diagnoses the carotid atherosclerosis with high performances. The DeepMAD network can be used in clinical trials to help radiologists get rid of tedious reading tasks, such as screening review to separate the normal carotid from the atherosclerotic arteries and outlining the vessel wall contours.
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Affiliation(s)
- Jiayi Wu
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jingmin Xin
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Jie Sun
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Dongxiang Xu
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Nanning Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, WA, USA
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11
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Directional fast-marching and multi-model strategy to extract coronary artery centerlines. Comput Biol Med 2019; 108:67-77. [DOI: 10.1016/j.compbiomed.2019.03.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 03/29/2019] [Accepted: 03/30/2019] [Indexed: 11/18/2022]
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12
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Sedghi Gamechi Z, Bons LR, Giordano M, Bos D, Budde RPJ, Kofoed KF, Pedersen JH, Roos-Hesselink JW, de Bruijne M. Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT. Eur Radiol 2019; 29:4613-4623. [PMID: 30673817 PMCID: PMC6682850 DOI: 10.1007/s00330-018-5931-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 11/08/2018] [Accepted: 11/29/2018] [Indexed: 01/15/2023]
Abstract
Objectives To develop and evaluate a fully automatic method to measure diameters of the ascending and descending aorta on non-ECG-gated, non-contrast computed tomography (CT) scans. Material and methods The method combines multi-atlas registration to obtain seed points, aorta centerline extraction, and an optimal surface segmentation approach to extract the aorta surface around the centerline. From the extracted 3D aorta segmentation, the diameter of the ascending and descending aorta was calculated at cross-sectional slices perpendicular to the extracted centerline, at the level of the pulmonary artery bifurcation, and at 1-cm intervals up to 3 cm above and below this level. Agreement with manual annotations was evaluated by dice similarity coefficient (DSC) for segmentation overlap, mean surface distance (MSD), and intra-class correlation (ICC) of diameters on 100 CT scans from a lung cancer screening trial. Repeatability of the diameter measurements was evaluated on 617 baseline-one year follow-up CT scan pairs. Results The agreement between manual and automatic segmentations was good with 0.95 ± 0.01 DSC and 0.56 ± 0.08 mm MSD. ICC between the diameters derived from manual and from automatic segmentations was 0.97, with the per-level ICC ranging from 0.87 to 0.94. An ICC of 0.98 for all measurements and per-level ICC ranging from 0.91 to 0.96 were obtained for repeatability. Conclusion This fully automatic method can assess diameters in the thoracic aorta reliably even in non-ECG-gated, non-contrast CT scans. This could be a promising tool to assess aorta dilatation in screening and in clinical practice. Key Points • Fully automatic method to assess thoracic aorta diameters. • High agreement between fully automatic method and manual segmentations. • Method is suitable for non-ECG-gated CT and can therefore be used in screening. Electronic supplementary material The online version of this article (10.1007/s00330-018-5931-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zahra Sedghi Gamechi
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
| | - Lidia R Bons
- Department of Cardiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marco Giordano
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Ricardo P J Budde
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Klaus F Kofoed
- Department of Cardiothoracic Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jesper Holst Pedersen
- Department of Cardiothoracic Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.,Machine Learning Section, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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Arias-Lorza AM, Bos D, van der Lugt A, de Bruijne M. Cooperative carotid artery centerline extraction in MRI. PLoS One 2018; 13:e0197180. [PMID: 29847545 PMCID: PMC5976187 DOI: 10.1371/journal.pone.0197180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 04/27/2018] [Indexed: 12/01/2022] Open
Abstract
Centerline extraction of the carotid artery in MRI is important to analyze the artery geometry and to provide input for further processing such as registration and segmentation. The centerline of the artery bifurcation is often extracted by means of two independent minimum cost paths ranging from the common to the internal and the external carotid artery. Often the cost is not well defined at the artery bifurcation, leading to centerline errors. To solve this problem, we developed a method to cooperatively extract both centerlines, where in the cost to extract each centerline, we integrate a constraint region derived from the estimated position of the neighbor centerline. This method avoids that both centerlines follow the same cheapest path after the bifurcation, which is a common error when the paths are extracted independently. We show that this method results in less error compared to extracting them independently: 10 failed centerlines Vs. 3 failures in a data set of 161 arteries with manual annotations. Additionally, we show that the new method improves the non-cooperative approach in 28 cases (p < 0.0001) in a data set of 3,904 arteries.
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Affiliation(s)
- Andrés M. Arias-Lorza
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- * E-mail:
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- Image Section, Department of Computer Science, University of Copenhagen, Denmark
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14
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Moccia S, De Momi E, El Hadji S, Mattos LS. Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:71-91. [PMID: 29544791 DOI: 10.1016/j.cmpb.2018.02.001] [Citation(s) in RCA: 211] [Impact Index Per Article: 35.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 12/23/2017] [Accepted: 02/02/2018] [Indexed: 05/09/2023]
Abstract
BACKGROUND Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). OBJECTIVE This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. METHODS This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. DISCUSSION Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. CONCLUSION No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.
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Affiliation(s)
- Sara Moccia
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Sara El Hadji
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
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15
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Arias Lorza AM, van Engelen A, Petersen J, van der Lugt A, de Bruijne M. Maximization of regional probabilities using Optimal Surface Graphs: Application to carotid artery segmentation in MRI. Med Phys 2018; 45:1159-1169. [DOI: 10.1002/mp.12771] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 11/21/2017] [Accepted: 12/26/2017] [Indexed: 11/10/2022] Open
Affiliation(s)
- Andres M. Arias Lorza
- Biomedical Imaging Group Rotterdam; Departments of Radiology and Medical Informatics; Erasmus MC; Rotterdam The Netherlands
| | - Arna van Engelen
- Biomedical Imaging Group Rotterdam; Departments of Radiology and Medical Informatics; Erasmus MC; Rotterdam The Netherlands
| | - Jens Petersen
- Department of Computer Science; University of Copenhagen; Copenhagen Denmark
| | | | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam; Departments of Radiology and Medical Informatics; Erasmus MC; Rotterdam The Netherlands
- Department of Computer Science; University of Copenhagen; Copenhagen Denmark
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16
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Carvalho DDB, Arias Lorza AM, Niessen WJ, de Bruijne M, Klein S. Automated Registration of Freehand B-Mode Ultrasound and Magnetic Resonance Imaging of the Carotid Arteries Based on Geometric Features. ULTRASOUND IN MEDICINE & BIOLOGY 2017; 43:273-285. [PMID: 27743726 DOI: 10.1016/j.ultrasmedbio.2016.08.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Revised: 07/30/2016] [Accepted: 08/29/2016] [Indexed: 06/06/2023]
Abstract
An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized in a cross-validation experiment. Next, the effect of the weighting parameter of the proposed registration dissimilarity metric and the control point spacing in the non-rigid registration was evaluated. Finally, the proposed registration method was evaluated in comparison to an existing intensity-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm for all subjects, 0.65 ± 0.09 mm for healthy volunteers and 0.87 ± 0.42 mm for patients. The results for the complete set were significantly better (Wilcoxon test, p < 0.01) than the results for the intensity-and-point-based method and the centerline-based registration method. We conclude that the proposed method can robustly and accurately register US and MR images of the carotid artery, allowing multimodal analysis of the carotid plaque to improve plaque assessment.
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Affiliation(s)
- Diego D B Carvalho
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Andres Mauricio Arias Lorza
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, The Netherlands.
| | - Wiro J Niessen
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, The Netherlands; Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, The Netherlands
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17
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Gao S, van 't Klooster R, Brandts A, Roes SD, Alizadeh Dehnavi R, de Roos A, Westenberg JJ, van der Geest RJ. Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline. J Magn Reson Imaging 2016; 45:215-228. [DOI: 10.1002/jmri.25332] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 05/20/2016] [Indexed: 11/08/2022] Open
Affiliation(s)
- Shan Gao
- Division of Image Processing; Department of Radiology, Leiden University Medical Center; Leiden Netherlands
| | - Ronald van 't Klooster
- Division of Image Processing; Department of Radiology, Leiden University Medical Center; Leiden Netherlands
| | - Anne Brandts
- Department of Radiology; Leiden University Medical Center; Leiden Netherlands
| | - Stijntje D. Roes
- Department of Radiology; Leiden University Medical Center; Leiden Netherlands
| | | | - Albert de Roos
- Department of Radiology; Leiden University Medical Center; Leiden Netherlands
| | - Jos J.M. Westenberg
- Division of Image Processing; Department of Radiology, Leiden University Medical Center; Leiden Netherlands
| | - Rob J. van der Geest
- Division of Image Processing; Department of Radiology, Leiden University Medical Center; Leiden Netherlands
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18
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Arias-Lorza AM, Petersen J, van Engelen A, Selwaness M, van der Lugt A, Niessen WJ, de Bruijne M. Carotid Artery Wall Segmentation in Multispectral MRI by Coupled Optimal Surface Graph Cuts. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:901-911. [PMID: 26595912 DOI: 10.1109/tmi.2015.2501751] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We present a new three-dimensional coupled optimal surface graph-cut algorithm to segment the wall of the carotid artery bifurcation from Magnetic Resonance (MR) images. The method combines the search for both inner and outer borders into a single graph cut and uses cost functions that integrate information from multiple sequences. Our approach requires manual localization of only three seed points indicating the start and end points of the segmentation in the internal, external, and common carotid artery. We performed a quantitative validation using images of 57 carotid arteries. Dice overlap of 0.86 ± 0.06 for the complete vessel and 0.89 ± 0.05 for the lumen compared to manual annotation were obtained. Reproducibility tests were performed in 60 scans acquired with an interval of 15 ± 9 days, showing good agreement between baseline and follow-up segmentations with intraclass correlations of 0.96 and 0.74 for the lumen and complete vessel volumes respectively.
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19
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Guo H, Wang G, Huang L, Hu Y, Yuan C, Li R, Zhao X. A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP) Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration. PLoS One 2016; 11:e0148783. [PMID: 26881433 PMCID: PMC4755573 DOI: 10.1371/journal.pone.0148783] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2015] [Accepted: 01/22/2016] [Indexed: 11/29/2022] Open
Abstract
Atherosclerosis is among the leading causes of death and disability. Combining information from multi-modal vascular images is an effective and efficient way to diagnose and monitor atherosclerosis, in which image registration is a key technique. In this paper a feature-based registration algorithm, Two-step Auto-labeling Conditional Iterative Closed Points (TACICP) algorithm, is proposed to align three-dimensional carotid image datasets from ultrasound (US) and magnetic resonance (MR). Based on 2D segmented contours, a coarse-to-fine strategy is employed with two steps: rigid initialization step and non-rigid refinement step. Conditional Iterative Closest Points (CICP) algorithm is given in rigid initialization step to obtain the robust rigid transformation and label configurations. Then the labels and CICP algorithm with non-rigid thin-plate-spline (TPS) transformation model is introduced to solve non-rigid carotid deformation between different body positions. The results demonstrate that proposed TACICP algorithm has achieved an average registration error of less than 0.2mm with no failure case, which is superior to the state-of-the-art feature-based methods.
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Affiliation(s)
- Hengkai Guo
- Research Institute of Image and Information, Department of Electrical Engineering, Tsinghua University, Beijing, China
| | - Guijin Wang
- Research Institute of Image and Information, Department of Electrical Engineering, Tsinghua University, Beijing, China
| | - Lingyun Huang
- Healthcare Department, Philips Research China, Shanghai, China
| | - Yuxin Hu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Chun Yuan
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
- Department of Radiology, University of Washington, 850 Republican St, Seattle, WA, United States of America
| | - Rui Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
- * E-mail:
| | - Xihai Zhao
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
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20
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Leung KYE, van der Lijn F, Vrooman HA, Sturkenboom MCJM, Niessen WJ. IT Infrastructure to support the secondary use of routinely acquired clinical imaging data for research. Neuroinformatics 2015; 13:65-81. [PMID: 25129841 PMCID: PMC4303741 DOI: 10.1007/s12021-014-9240-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
We propose an infrastructure for the automated anonymization, extraction and processing of image data stored in clinical data repositories to make routinely acquired imaging data available for research purposes. The automated system, which was tested in the context of analyzing routinely acquired MR brain imaging data, consists of four modules: subject selection using PACS query, anonymization of privacy sensitive information and removal of facial features, quality assurance on DICOM header and image information, and quantitative imaging biomarker extraction. In total, 1,616 examinations were selected based on the following MRI scanning protocols: dementia protocol (246), multiple sclerosis protocol (446) and open question protocol (924). We evaluated the effectiveness of the infrastructure in accessing and successfully extracting biomarkers from routinely acquired clinical imaging data. To examine the validity, we compared brain volumes between patient groups with positive and negative diagnosis, according to the patient reports. Overall, success rates of image data retrieval and automatic processing were 82.5 %, 82.3 % and 66.2 % for the three protocol groups respectively, indicating that a large percentage of routinely acquired clinical imaging data can be used for brain volumetry research, despite image heterogeneity. In line with the literature, brain volumes were found to be significantly smaller (p-value <0.001) in patients with a positive diagnosis of dementia (915 ml) compared to patients with a negative diagnosis (939 ml). This study demonstrates that quantitative image biomarkers such as intracranial and brain volume can be extracted from routinely acquired clinical imaging data. This enables secondary use of clinical images for research into quantitative biomarkers at a hitherto unprecedented scale.
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Affiliation(s)
- Kai Yan Eugene Leung
- Department of Medical Informatics, Erasmus MC: University Medical Center Rotterdam, Dr. Molewaterplein 50, Building NA, Room NA2502, 3015 GE, Rotterdam, Zuid-Holland, The Netherlands,
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21
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Ukwatta E, Yuan J, Qiu W, Rajchl M, Chiu B, Fenster A. Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease. Med Image Anal 2015; 26:120-32. [PMID: 26387053 DOI: 10.1016/j.media.2015.08.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Revised: 08/17/2015] [Accepted: 08/19/2015] [Indexed: 10/23/2022]
Abstract
Three-dimensional (3D) measurements of peripheral arterial disease (PAD) plaque burden extracted from fast black-blood magnetic resonance (MR) images have shown to be more predictive of clinical outcomes than PAD stenosis measurements. To this end, accurate segmentation of the femoral artery lumen and outer wall is required for generating volumetric measurements of PAD plaque burden. Here, we propose a semi-automated algorithm to jointly segment the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, which are reoriented and reconstructed along the medial axis of the femoral artery to obtain improved spatial coherence between slices of the long, thin femoral artery and to reduce computation time. The developed segmentation algorithm enforces two priors in a global optimization manner: the spatial consistency between the adjacent 2D slices and the anatomical region order between the femoral artery lumen and outer wall surfaces. The formulated combinatorial optimization problem for segmentation is solved globally and exactly by means of convex relaxation using a coupled continuous max-flow (CCMF) model, which is a dual formulation to the convex relaxed optimization problem. In addition, the CCMF model directly derives an efficient duality-based algorithm based on the modern multiplier augmented optimization scheme, which has been implemented on a GPU for fast computation. The computed segmentations from the developed algorithm were compared to manual delineations from experts using 20 black-blood MR images. The developed algorithm yielded both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area), while outperforming the state-of-the-art method in terms of computational time by a factor of ≈ 20.
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Affiliation(s)
- Eranga Ukwatta
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
| | - Jing Yuan
- Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada
| | - Wu Qiu
- Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada
| | - Martin Rajchl
- Department of Computing, Imperial College London, London, UK
| | - Bernard Chiu
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong
| | - Aaron Fenster
- Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada
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Topology adaptive vessel network skeleton extraction with novel medialness measuring function. Comput Biol Med 2015; 64:40-61. [PMID: 26134626 DOI: 10.1016/j.compbiomed.2015.06.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2015] [Revised: 06/04/2015] [Accepted: 06/05/2015] [Indexed: 11/22/2022]
Abstract
Vessel tree skeleton extraction is widely applied in vascular structure segmentation, however, conventional approaches often suffer from the adjacent interferences and poor topological adaptability. To avoid these problems, a robust, topology adaptive tree-like structure skeleton extraction framework is proposed in this paper. Specifically, to avoid the adjacent interferences, a local message passing procedure called Gaussian affinity voting (GAV) is proposed to realize adaptive scale-growing of vessel voxels. Then the medialness measuring function (MMF) based on GAV, namely GAV-MMF, is constructed to extract medialness patterns robustly. In order to improve topological adaptability, a level-set graph embedded with GAV-MMF is employed to build initial curve skeletons without any user interaction. Furthermore, the GAV-MMF is embedded in stretching open active contours (SOAC) to drive the initial curves to the expected location, maintaining smoothness and continuity. In addition, to provide an accurate and smooth final skeleton tree topology, topological checks and skeleton network reconfiguration is proposed. The continuity and scalability of this method is validated experimentally on synthetic and clinical images for multi-scale vessels. Experimental results show that the proposed method achieves acceptable topological adaptability for skeleton extraction of vessel trees.
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23
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ZHANG DONG, DOU KEFEI. Coronary Bifurcation Intervention: What Role Do Bifurcation Angles Play? J Interv Cardiol 2015; 28:236-48. [DOI: 10.1111/joic.12203] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- DONG ZHANG
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease, Cardiovascular Institute; Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College; Beijing 100037 China
| | - KEFEI DOU
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease, Cardiovascular Institute; Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College; Beijing 100037 China
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24
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Carvalho DDB, Klein S, Akkus Z, van Dijk AC, Tang H, Selwaness M, Schinkel AFL, Bosch JG, van der Lugt A, Niessen WJ. Joint intensity-and-point based registration of free-hand B-mode ultrasound and MRI of the carotid artery. Med Phys 2014; 41:052904. [PMID: 24784404 DOI: 10.1118/1.4870383] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
PURPOSE To introduce a semiautomatic algorithm to perform the registration of free-hand B-Mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid artery. METHODS The authors' approach combines geometrical features and intensity information. The only user interaction consists of placing three seed points in US and MRI. First, the lumen centerlines are used as landmarks for point based registration. Subsequently, in a joint optimization the distance between centerlines and the dissimilarity of the image intensities is minimized. Evaluation is performed in left and right carotids from six healthy volunteers and five patients with atherosclerosis. For the validation, the authors measure the Dice similarity coefficient (DSC) and the mean surface distance (MSD) between carotid lumen segmentations in US and MRI after registration. The effect of several design parameters on the registration accuracy is investigated by an exhaustive search on a training set of five volunteers and three patients. The optimum configuration is validated on the remaining images of one volunteer and two patients. RESULTS On the training set, the authors achieve an average DSC of 0.74 and a MSD of 0.66 mm on volunteer data. For the patient data, the authors obtain a DSC of 0.77 and a MSD of 0.69 mm. In the independent set composed of patient and volunteer data, the DSC is 0.69 and the MSD is 0.87 mm. The experiments with different design parameters show that nonrigid registration outperforms rigid registration, and that the combination of intensity and point information is superior to approaches that use intensity or points only. CONCLUSIONS The proposed method achieves an accurate registration of US and MRI, and may thus enable multimodal analysis of the carotid plaque.
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Affiliation(s)
- Diego D B Carvalho
- Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Stefan Klein
- Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Zeynettin Akkus
- Biomedical Engineering, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Anouk C van Dijk
- Department of Radiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands and Department of Neurology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Hui Tang
- Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 CE, The Netherlands and Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft 2600 AA, The Netherlands
| | - Mariana Selwaness
- Department of Radiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Arend F L Schinkel
- Department of Internal Medicine, Division of Pharmacology, Vascular and Metabolic Diseases, Erasmus MC, Rotterdam 3015 CE, The Netherlands and Department of Cardiology, Thoraxcenter, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Johan G Bosch
- Biomedical Engineering, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Aad van der Lugt
- Department of Radiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands
| | - Wiro J Niessen
- Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 CE, The Netherlands and Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft 2600 AA, The Netherlands
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Dibildox G, Baka N, Punt M, Aben JP, Schultz C, Niessen W, van Walsum T. 3D/3D registration of coronary CTA and biplane XA reconstructions for improved image guidance. Med Phys 2014; 41:091909. [DOI: 10.1118/1.4892055] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Tang H, van Walsum T, Hameeteman R, Shahzad R, van Vliet LJ, Niessen WJ. Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior. Med Phys 2013; 40:051721. [PMID: 23635269 DOI: 10.1118/1.4802751] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images. METHODS The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion. RESULTS The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification. CONCLUSIONS With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.
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Affiliation(s)
- Hui Tang
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
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van ˈt Klooster R, Staring M, Klein S, Kwee RM, Kooi ME, Reiber JHC, Lelieveldt BPF, van der Geest RJ. Automated registration of multispectral MR vessel wall images of the carotid artery. Med Phys 2013; 40:121904. [DOI: 10.1118/1.4829503] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Liu L, Shi W, Rueckert D, Hu M, Ourselin S, Zhuang X. Model-guided directional minimal path for fully automatic extraction of coronary centerlines from cardiac CTA. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2013; 16:542-549. [PMID: 24505709 DOI: 10.1007/978-3-642-40811-3_68] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Extracting centerlines of coronary arteries is a challenging but important task in clinical applications of cardiac CTA. In this paper, we propose a model-guided approach, the directional minimal path, for the centerline extraction. The proposed method is based on the minimal path algorithm and a prior coronary model is used. The model is first registered to the unseen image. Then, the start point and end point for the minimal path algorithm are provided by the model to automate the centerline extraction process. Also, the direction information of the coronary model is used to guide the path tracking of the minimal path procedure. This directional tracking improves the robustness and accuracy of the centerline extraction. Finally, the proposed method can automatically recognize the branches of the extracted coronary artery using the prior information in the model. We validated the proposed method by extracting the three main coronary branches. The mean accuracy of the 56 cases was 1.32+/-0.81 mm and the detection ratio was 88.7%.
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Affiliation(s)
- Liu Liu
- Shanghai Jiaotong Universiy, Shanghai, China
| | - Wenzhe Shi
- Biomedical Image Analysis Group, Imperial College London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, UK
| | - Mingxing Hu
- Centre for Medical Image Computing, University College London, UK
| | | | - Xiahai Zhuang
- Shanghai Advanced Research Institute, Chinese Academy of Sciences, China
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