1
|
Wu J, Xin J, Yang X, Matkovic LA, Zhao X, Zheng N, Li R. Segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black blood magnetic resonance imaging with multi-task learning. Med Phys 2024; 51:1775-1797. [PMID: 37681965 DOI: 10.1002/mp.16728] [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: 03/29/2023] [Revised: 07/04/2023] [Accepted: 07/29/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Atherosclerotic cardiovascular disease is the leading cause of death worldwide. Early detection of carotid atherosclerosis can prevent the progression of cardiovascular disease. Many (semi-) automatic methods have been designed for the segmentation of carotid vessel wall and the diagnosis of carotid atherosclerosis (i.e., the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis) on black blood magnetic resonance imaging (BB-MRI). However, most of these methods ignore the intrinsic correlation among different tasks on BB-MRI, leading to limited performance. PURPOSE Thus, we model the intrinsic correlation among the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis tasks on BB-MRI by using the multi-task learning technique and propose a gated multi-task network (GMT-Net) to perform three related tasks in a neural network (i.e., carotid artery lumen segmentation, outer wall segmentation, and carotid atherosclerosis diagnosis). METHODS In the proposed method, the GMT-Net is composed of three modules, including the sharing module, the segmentation module, and the diagnosis module, which interact with each other to achieve better learning performance. At the same time, two new adaptive layers, namely, the gated exchange layer and the gated fusion layer, are presented to exchange and merge branch features. RESULTS The proposed method is applied to the CAREII dataset (i.e., 1057 scans) for the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis. The proposed method can achieve promising segmentation performances (0.9677 Dice for the lumen and 0.9669 Dice for the outer wall) and better diagnosis accuracy of carotid atherosclerosis (0.9516 AUC and 0.9024 Accuracy) in the "CAREII test" dataset (i.e., 106 scans). The results show that the proposed method has statistically significant accuracy and efficiency. CONCLUSIONS Even without the intervention of reviewers required for the previous works, the proposed method automatically segments the lumen and outer wall together and diagnoses carotid atherosclerosis with high performance. The proposed method can be used in clinical trials to help radiologists get rid of tedious reading tasks, such as screening review to separate normal carotid arteries from atherosclerotic arteries and to outline vessel wall contours.
Collapse
Affiliation(s)
- Jiayi Wu
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University
| | - Jingmin Xin
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Luke A Matkovic
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Xihai Zhao
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Nanning Zheng
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University
| | - Rui Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| |
Collapse
|
2
|
Camarasa R, Kervadec H, Kooi ME, Hendrikse J, Nederkoorn PJ, Bos D, de Bruijne M. Nested star-shaped objects segmentation using diameter annotations. Med Image Anal 2023; 90:102934. [PMID: 37688981 DOI: 10.1016/j.media.2023.102934] [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: 12/22/2022] [Revised: 08/02/2023] [Accepted: 08/10/2023] [Indexed: 09/11/2023]
Abstract
Most current deep learning based approaches for image segmentation require annotations of large datasets, which limits their application in clinical practice. We observe a mismatch between the voxelwise ground-truth that is required to optimize an objective at a voxel level and the commonly used, less time-consuming clinical annotations seeking to characterize the most important information about the patient (diameters, counts, etc.). In this study, we propose to bridge this gap for the case of multiple nested star-shaped objects (e.g., a blood vessel lumen and its outer wall) by optimizing a deep learning model based on diameter annotations. This is achieved by extracting in a differentiable manner the boundary points of the objects at training time, and by using this extraction during the backpropagation. We evaluate the proposed approach on segmentation of the carotid artery lumen and wall from multisequence MR images, thus reducing the annotation burden to only four annotated landmarks required to measure the diameters in the direction of the vessel's maximum narrowing. Our experiments show that training based on diameter annotations produces state-of-the-art weakly supervised segmentations and performs reasonably compared to full supervision. We made our code publicly available at https://gitlab.com/radiology/aim/carotid-artery-image-analysis/nested-star-shaped-objects.
Collapse
Affiliation(s)
- Robin Camarasa
- Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
| | - Hoel Kervadec
- Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - M Eline Kooi
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Paul J Nederkoorn
- Department of Neurology, Academic Medical Center University of Amsterdam, Amsterdam, The Netherlands
| | - Daniel Bos
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Denmark.
| |
Collapse
|
3
|
Zhang P, Xin J, Wu J, Zheng N. A Space-Refine Paradigm for Automatic Carotid Artery Centerline Extraction in Magnetic Resonance Imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083165 DOI: 10.1109/embc40787.2023.10340577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Vessel centerline extraction is essential for carotid stenosis assessment and atherosclerotic plaque identification in clinical diagnosis. Simultaneously, it provides a region of interest identification and boundary initialization for computer-assisted diagnosis tools. In magnetic resonance imaging (MRI) cross-sectional images, the lumen shape and vascular topology result in a challenging task to extract the centerline accurately. To this end, we propose a space-refine framework, which exploits the positional continuity of the carotid artery from frame to frame to extract the carotid artery centerline. The proposed framework consists of a detector and a refinement module. Specifically, the detector roughly extracts the carotid lumen region from the original image. Then, we introduce a refinement module that uses the cascade of regressors from a detector to perform sequence realignment of lumen bounding boxes for each subject. It improves the lumen localization results and further enhances the centerline extraction accuracy. Verified by large carotid artery data, the proposed framework achieves state-of-the-art performance compared to conventional vessel centerline extraction methods or standard convolutional neural network approaches.Clinical relevance- Our proposed framework can be used as an important aid for physicians to quantitatively analyze the carotid artery in clinical practice. It is also used as a new paradigm for extracting the centerline of carotid vessels in computer-assisted tools.
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
Zhu C, Wang X, Chen S, Teng Z, Bai C, Huang X, Xia M, Shao Z, Gu Z, Sun P. Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning. Med Biol Eng Comput 2022; 60:2693-2706. [DOI: 10.1007/s11517-022-02622-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 04/28/2022] [Indexed: 11/30/2022]
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Latha S, Samiappan D, Muthu P, Kumar R. Fully Automated Integrated Segmentation of Carotid Artery Ultrasound Images Using DBSCAN and Affinity Propagation. J Med Biol Eng 2021. [DOI: 10.1007/s40846-020-00586-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Abstract
Purpose
B-mode ultrasound images are used in identifying the presence of fat deposit if any in carotid artery. The intima media, lumen, bifurcation boundary is detected by the echogenic characteristics embedded in the carotid artery.
Methods
A fully automatic self-learning based segmentation is proposed by extracting the edges by a modified affinity propagation, which are given as inputs to the Density Based Spatial Clustering of Applications with Noise (DBSCAN) for super pixel segmentation. The segmented results are analyzed with Gradient Vector Flow (GVF) snake model and Particle Swarm Optimization (PSO) clustering based segmentation using various performance measures.
Results
The proposed parameter free, fully automatic segmentation method combining Affinity propagation and DBSCAN are evaluated for a database of 361 images and gives reinforced results in the longitudinal B-mode ultrasound images. The proposed approach gives an improved accuracy of 12% increase when compared with the manual segmentation and 15% compared with segmentation by affinity propagation and DBSCAN when performed individually. The average Root Mean Square Error (RMSE) is 110 ± 44 µm.
Conclusion
Extracted edge points are used for clustering in a fully automated carotid artery segmentation approach.
Collapse
|
9
|
Chen L, Sun J, Canton G, Balu N, Hippe DS, Zhao X, Li R, Hatsukami TS, Hwang JN, Yuan C. Automated Artery Localization and Vessel Wall Segmentation using Tracklet Refinement and Polar Conversion. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:217603-217614. [PMID: 33777593 PMCID: PMC7996631 DOI: 10.1109/access.2020.3040616] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.
Collapse
Affiliation(s)
- Li Chen
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Jie Sun
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Gador Canton
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Niranjan Balu
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Daniel S. Hippe
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Xihai Zhao
- Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China
| | - Rui Li
- Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China
| | | | - Jenq-Neng Hwang
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| |
Collapse
|
10
|
Chen L, Canton G, Liu W, Hippe DS, Balu N, Watase H, Hatsukami TS, Waterton JC, Hwang JN, Yuan C. Fully automated and robust analysis technique for popliteal artery vessel wall evaluation (FRAPPE) using neural network models from standardized knee MRI. Magn Reson Med 2020; 84:2147-2160. [PMID: 32162395 PMCID: PMC8320767 DOI: 10.1002/mrm.28237] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/27/2020] [Accepted: 02/07/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a fully automated vessel wall (VW) analysis workflow (fully automated and robust analysis technique for popliteal artery evaluation, FRAPPE) on the popliteal artery in standardized knee MR images. METHODS Popliteal artery locations were detected from each MR slice by a deep neural network model and connected into a 3D artery centerline. Vessel wall regions around the centerline were then segmented using another neural network model for segmentation in polar coordinate system. Contours from vessel wall segmentations were used for vascular feature calculation, such as mean wall thickness and wall area. A transfer learning and active learning framework was applied in training the localization and segmentation neural network models to maintain accuracy while reducing manual annotations. This new popliteal artery analysis technique (FRAPPE) was validated against manual segmentation qualitatively and quantitatively in a series of 225 cases from the Osteoarthritis Initiative (OAI) dataset. RESULTS FRAPPE demonstrated high accuracy and robustness in locating popliteal arteries, segmenting artery walls, and quantifying arterial features. Qualitative evaluations showed 1.2% of slices had noticeable major errors, including segmenting the wrong target and irregular vessel wall contours. The mean Dice similarity coefficient with manual segmentation was 0.79, which is comparable to inter-rater variations. Repeatability evaluations show most of the vascular features have good to excellent repeatability from repeated scans of same subjects, with intra-class coefficient ranging from 0.80 to 0.98. CONCLUSION This technique can be used in large population-based studies, such as OAI, to efficiently assess the burden of atherosclerosis from routine MR knee scans.
Collapse
Affiliation(s)
- Li Chen
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Gador Canton
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Wenjin Liu
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Daniel S. Hippe
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Niranjan Balu
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Hiroko Watase
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | | | - John C. Waterton
- Centre for Imaging Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Jenq-Neng Hwang
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, Washington, USA
| |
Collapse
|
11
|
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.
Collapse
|
12
|
Jodas DS, da Costa MFM, Parreira TAA, Pereira AS, Tavares JMRS. Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images. Comput Biol Med 2020; 123:103901. [PMID: 32658794 DOI: 10.1016/j.compbiomed.2020.103901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/20/2020] [Accepted: 06/28/2020] [Indexed: 10/23/2022]
Abstract
Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 ± 0.11, 2.70 ± 1.69 pixels, 2.79 ± 1.89 pixels and 3.44 ± 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.
Collapse
Affiliation(s)
- Danilo Samuel Jodas
- CAPES Foundation, Ministry of Education of Brazil, Brasília - DF, 70040-020, Brazil; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
| | - Maria Francisca Monteiro da Costa
- IFE Neurorradiologia, Serviço de Neurorradiologia, Centro Hospitalar São João, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal.
| | - Tiago A A Parreira
- AH Neurorradiologia, Serviço de Neurorradiologia, Centro Hospitalar São João, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal.
| | - Aledir Silveira Pereira
- Universidade Estadual Paulista Júlio de Mesquita Filho, Rua Cristóvão Colombo, 2265, 15054-000, S. J. do Rio Preto, Brazil.
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
| |
Collapse
|
13
|
Latha S, Samiappan D, Kumar R. Carotid artery ultrasound image analysis: A review of the literature. Proc Inst Mech Eng H 2020; 234:417-443. [PMID: 31960771 DOI: 10.1177/0954411919900720] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima-media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima-media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning-based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.
Collapse
Affiliation(s)
- S Latha
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
| | - Dhanalakshmi Samiappan
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
| | - R Kumar
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
| |
Collapse
|
14
|
Ning Q, Yu X, Gao Q, Xie J, Yao C, Zhou K, Ye J. An accurate interactive segmentation and volume calculation of orbital soft tissue for orbital reconstruction after enucleation. BMC Ophthalmol 2019; 19:256. [PMID: 31842802 PMCID: PMC6916112 DOI: 10.1186/s12886-019-1260-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Accepted: 11/27/2019] [Indexed: 12/18/2022] Open
Abstract
Background Accurate measurement and reconstruction of orbital soft tissue is important to diagnosis and treatment of orbital diseases. This study applied an interactive graph cut method to orbital soft tissue precise segmentation and calculation in computerized tomography (CT) images, and to estimate its application in orbital reconstruction. Methods The interactive graph cut method was introduced to segment extraocular muscle and intraorbital fat in CT images. Intra- and inter-observer variability of tissue volume measured by graph cut segmentation was validated. Accuracy and reliability of the method was accessed by comparing with manual delineation and commercial medical image software. Intraorbital structure of 10 patients after enucleation surgery was reconstructed based on graph cut segmentation and soft tissue volume were compared within two different surgical techniques. Results Both muscle and fat tissue segmentation results of graph cut method showed good consistency with ground truth in phantom data. There were no significant differences in muscle calculations between observers or segmental methods (p > 0.05). Graph cut results of fat tissue had coincidental variable trend with ground truth which could identify 0.1cm3 variation. The mean performance time of graph cut segmentation was significantly shorter than manual delineation and commercial software (p < 0.001). Jaccard similarity and Dice coefficient of graph cut method were 0.767 ± 0.045 and 0.836 ± 0.032 for human normal extraocular muscle segmentation. The measurements of fat tissue were significantly better in graph cut than those in commercial software (p < 0.05). Orbital soft tissue volume was decreased in post-enucleation orbit than that in normal orbit (p < 0.05). Conclusion The graph cut method was validated to have good accuracy, reliability and efficiency in orbit soft tissue segmentation. It could discern minor volume changes of soft tissue. The interactive segmenting technique would be a valuable tool for dynamic analysis and prediction of therapeutic effect and orbital reconstruction.
Collapse
Affiliation(s)
- Qingyao Ning
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang Province, China
| | - Xiaoyao Yu
- State Key Lab of CAD & CG, Zhejiang University, No. 886 Yuhangtang Road, Hangzhou, 310058, Zhejiang Province, China
| | - Qi Gao
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang Province, China
| | - Jiajun Xie
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang Province, China
| | - Chunlei Yao
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang Province, China
| | - Kun Zhou
- State Key Lab of CAD & CG, Zhejiang University, No. 886 Yuhangtang Road, Hangzhou, 310058, Zhejiang Province, China.
| | - Juan Ye
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang Province, China.
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
Petersen J, Arias-Lorza AM, Selvan R, Bos D, van der Lugt A, Pedersen JH, Nielsen M, de Bruijne M. Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1559-1568. [PMID: 30605096 DOI: 10.1109/tmi.2018.2890386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Optimal surface methods are a class of graph cut methods posing surface estimation as an n-ary ordered labeling problem. They are used in medical imaging to find interacting and layered surfaces optimally and in low order polynomial time. Representing continuous surfaces with discrete sets of labels, however, leads to discretization errors and, if graph representations are made dense, excessive memory usage. Limiting memory usage and computation time of graph cut methods are important and graphs that locally adapt to the problem has been proposed as a solution. Min-marginal energies computed using dynamic graph cuts offer a way to estimate solution uncertainty and these uncertainties have been used to decide where graphs should be adapted. Adaptive graphs, however, introduce extra parameters, complexity, and heuristics. We propose a way to use min-marginal energies to estimate continuous solution labels that does not introduce extra parameters and show empirically on synthetic and medical imaging datasets that it leads to improved accuracy. The increase in accuracy was consistent and in many cases comparable with accuracy otherwise obtained with graphs up to eight times denser, but with proportionally less memory usage and improvements in computation time.
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
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
| |
Collapse
|
19
|
Morais P, Vilaça JL, Queirós S, Bourier F, Deisenhofer I, Tavares JMRS, D'hooge J. A competitive strategy for atrial and aortic tract segmentation based on deformable models. Med Image Anal 2017; 42:102-116. [PMID: 28780174 DOI: 10.1016/j.media.2017.07.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Revised: 06/30/2017] [Accepted: 07/26/2017] [Indexed: 01/27/2023]
Abstract
Multiple strategies have previously been described for atrial region (i.e. atrial bodies and aortic tract) segmentation. Although these techniques have proven their accuracy, inadequate results in the mid atrial walls are common, restricting their application for specific cardiac interventions. In this work, we introduce a novel competitive strategy to perform atrial region segmentation with correct delineation of the thin mid walls, and integrated it into the B-spline Explicit Active Surfaces framework. A double-stage segmentation process is used, which starts with a fast contour growing followed by a refinement stage with local descriptors. Independent functions are used to define each region, being afterward combined to compete for the optimal boundary. The competition locally constrains the surface evolution, prevents overlaps and allows refinement to the walls. Three different scenarios were used to demonstrate the advantages of the proposed approach, through the evaluation of its segmentation accuracy, and its performance for heterogeneous mid walls. Both computed tomography and magnetic resonance imaging datasets were used, presenting results similar to the state-of-the-art methods for both atria and aorta. The competitive strategy showed its superior performance with statistically significant differences against the traditional free-evolution approach in cases with bad image quality or missed atrial/aortic walls. Moreover, only the competitive approach was able to accurately segment the atrial/aortic wall. Overall, the proposed strategy showed to be suitable for atrial region segmentation with a correct segmentation of the mid thin walls, demonstrating its added value with respect to the traditional techniques.
Collapse
Affiliation(s)
- Pedro Morais
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; Lab on Cardiovascular Imaging & Dynamics, Department of Cardiovascular Sciences, KULeuven - University of Leuven, Leuven, Belgium.
| | - João L Vilaça
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; DIGARC - Polytechnic Institute of Cávado and Ave, Barcelos, Portugal
| | - Sandro Queirós
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; Lab on Cardiovascular Imaging & Dynamics, Department of Cardiovascular Sciences, KULeuven - University of Leuven, Leuven, Belgium; Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Felix Bourier
- Department of Electrophysiology, German Heart Center Munich, Technical University, Munich, Germany
| | - Isabel Deisenhofer
- Department of Electrophysiology, German Heart Center Munich, Technical University, Munich, Germany
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
| | - Jan D'hooge
- Lab on Cardiovascular Imaging & Dynamics, Department of Cardiovascular Sciences, KULeuven - University of Leuven, Leuven, Belgium
| |
Collapse
|
20
|
Gao S, van 't Klooster R, Kitslaar PH, Coolen BF, van den Berg AM, Smits LP, Shahzad R, Shamonin DP, de Koning PJH, Nederveen AJ, van der Geest RJ. Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting. Med Phys 2017; 44:5244-5259. [PMID: 28715090 DOI: 10.1002/mp.12476] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 07/10/2017] [Accepted: 07/10/2017] [Indexed: 01/24/2023] Open
Abstract
PURPOSE The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI. METHODS The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis. RESULTS For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections. CONCLUSIONS The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers.
Collapse
Affiliation(s)
- Shan Gao
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Ronald van 't Klooster
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Pieter H Kitslaar
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Bram F Coolen
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | | | - Loek P Smits
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | - Rahil Shahzad
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Denis P Shamonin
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Patrick J H de Koning
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Aart J Nederveen
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | - Rob J van der Geest
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| |
Collapse
|
21
|
Mujaj B, Lorza AMA, van Engelen A, de Bruijne M, Franco OH, van der Lugt A, Vernooij MW, Bos D. Comparison of CT and CMR for detection and quantification of carotid artery calcification: the Rotterdam Study. J Cardiovasc Magn Reson 2017; 19:28. [PMID: 28260526 PMCID: PMC5338077 DOI: 10.1186/s12968-017-0340-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 02/10/2017] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Carotid artery atherosclerosis is an important risk factor for stroke. As such, quantitative imaging of carotid artery calcification, as a proxy of atherosclerosis, has become a cornerstone of current stroke research. Yet, population-based data comparing the computed tomography (CT) and cardiovascular magnetic resonance (CMR) for the detection and quantification of calcification remain scarce. METHODS A total of 684 participants from the population-based Rotterdam Study underwent both a CT and CMR of the carotid artery bifurcation to quantify the amount of carotid artery calcification (mean interscan interval: 4.9 ± 1.2 years). We investigated the correlation between the amount of calcification measured on CT and CMR using Spearman's correlation coefficient, Bland-Altman plots, and linear regression. In addition, using logistic regression modeling, we assessed the association of CT and CMR based calcification volumes with a history of stroke. RESULTS We found a strong correlation between CT and CMR based calcification volumes (Spearman's correlation coefficient:0.86, p-value ≤0.01). Bland-Altman analyses showed a good agreement, though CT based calcification volumes were systematically larger. Finally, calcification volume assessed with either imaging modality was associated with a history of stroke with similar effect estimates (odds ratio (OR) per 1-SD increase in calcification volume: 1.52 (95% CI:1.00;2.30) for CT, and 1.47 (95% CI:1.01;2.14) for CMR. CONCLUSION CT based and CMR based volumes of carotid artery calcification are highly correlated, but CMR based calcification is systematically smaller than those obtained with CT. Despite this difference, both provide comparable information with regard to a history of stroke.
Collapse
Affiliation(s)
- Blerim Mujaj
- Department of Epidemiology, Erasmus MC, University Medical Center, Office Na 2824k, PO Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Andrés M. Arias Lorza
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics, Radiology, and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Arna van Engelen
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics, Radiology, and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics, Radiology, and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus MC, University Medical Center, Office Na 2824k, PO Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus MC, University Medical Center, Office Na 2824k, PO Box 2040, 3000 CA Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC, University Medical Center, Office Na 2824k, PO Box 2040, 3000 CA Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Clinical Epidemiology, Harvard TH Chan School of Public Health, Boston, USA
| |
Collapse
|
22
|
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.
Collapse
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
| |
Collapse
|