1
|
Zhao Y, Jiang M, Chan WS, Chiu B. Development of a Three-Dimensional Carotid Ultrasound Image Segmentation Workflow for Improved Efficiency, Reproducibility and Accuracy in Measuring Vessel Wall and Plaque Volume and Thickness. Bioengineering (Basel) 2023; 10:1217. [PMID: 37892947 PMCID: PMC10603859 DOI: 10.3390/bioengineering10101217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/29/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation of LIB and MAB. Therefore, there is a need to improve the efficiency of manual segmentation and develop strategies to improve segmentation accuracy by the CNN for serial monitoring of carotid atherosclerosis. One strategy to reduce segmentation time is to increase the interslice distance (ISD) between segmented axial slices of a 3DUS image while maintaining the segmentation reliability. We, for the first time, investigated the effect of ISD on the reproducibility of MAB and LIB segmentations. The intra-observer reproducibility of LIB and MAB segmentations at ISDs of 1 mm and 2 mm was not statistically significantly different, whereas the reproducibility at ISD = 3 mm was statistically lower. Therefore, we conclude that segmentation with an ISD of 2 mm provides sufficient reliability for CNN training. We further proposed training the CNN by the baseline images of the entire cohort of patients for automatic segmentation of the follow-up images acquired for the same cohort. We validated that segmentation with this time-based partitioning approach is more accurate than that produced by patient-based partitioning, especially at the carotid bifurcation. This study forms the basis for an efficient, reproducible, and accurate 3DUS workflow for serial monitoring of carotid atherosclerosis useful in risk stratification of cardiovascular events and in evaluating the efficacy of new treatments.
Collapse
Affiliation(s)
- Yuan Zhao
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong; (Y.Z.); (M.J.); (W.S.C.)
| | - Mingjie Jiang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong; (Y.Z.); (M.J.); (W.S.C.)
| | - Wai Sum Chan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong; (Y.Z.); (M.J.); (W.S.C.)
| | - Bernard Chiu
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong; (Y.Z.); (M.J.); (W.S.C.)
- Department of Physics & Computer Science, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
| |
Collapse
|
2
|
He Z, Luo J, Lv M, Li Q, Ke W, Niu X, Zhang Z. Characteristics and evaluation of atherosclerotic plaques: an overview of state-of-the-art techniques. Front Neurol 2023; 14:1159288. [PMID: 37900593 PMCID: PMC10603250 DOI: 10.3389/fneur.2023.1159288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 09/28/2023] [Indexed: 10/31/2023] Open
Abstract
Atherosclerosis is an important cause of cerebrovascular and cardiovascular disease (CVD). Lipid infiltration, inflammation, and altered vascular stress are the critical mechanisms that cause atherosclerotic plaque formation. The hallmarks of the progression of atherosclerosis include plaque ulceration, rupture, neovascularization, and intraplaque hemorrhage, all of which are closely associated with the occurrence of CVD. Assessing the severity of atherosclerosis and plaque vulnerability is crucial for the prevention and treatment of CVD. Integrating imaging techniques for evaluating the characteristics of atherosclerotic plaques with computer simulations yields insights into plaque inflammation levels, spatial morphology, and intravascular stress distribution, resulting in a more realistic and accurate estimation of plaque state. Here, we review the characteristics and advancing techniques used to analyze intracranial and extracranial atherosclerotic plaques to provide a comprehensive understanding of atheroma.
Collapse
Affiliation(s)
- Zhiwei He
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiaying Luo
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengna Lv
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qingwen Li
- Department of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Ke
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xuan Niu
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhaohui Zhang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
3
|
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.
Collapse
|
4
|
Anand KS, Torres G, Homeister JW, Caughey MC, Gallippi CM. Comparing Focused-Tracked and Plane Wave-Tracked ARFI Log(VoA) In Silico and in Application to Human Carotid Atherosclerotic Plaque, Ex Vivo. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2023; 70:636-652. [PMID: 37216241 PMCID: PMC10330788 DOI: 10.1109/tuffc.2023.3278495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
A significant risk factor for ischemic stroke is carotid atherosclerotic plaque that is susceptible to rupture, with rupture potential conveyed by plaque morphology. Human carotid plaque composition and structure have been delineated noninvasively and in vivo by evaluating log(VoA), a parameter derived as the decadic log of the second time derivative of displacement induced by an acoustic radiation force impulse (ARFI). In prior work, ARFI-induced displacement was measured using conventional focused tracking; however, this requires a long data acquisition period, thereby reducing framerate. We herein evaluate if ARFI log(VoA) framerate can be increased without a reduction in plaque imaging performance using plane wave tracking instead. In silico, both focused- and plane wave-tracked log(VoA) decreased with increasing echobrightness, quantified as signal-to-noise ratio (SNR), but did not vary with material elasticity for SNRs below 40 dB. For SNRs of 40-60 dB, both focused- and plane wave-tracked log(VoA) varied with SNR and material elasticity. Above 60 dB SNR, both focused- and plane wave-tracked log(VoA) varied with material elasticity alone. This suggests that log(VoA) discriminates features according to a combination of their echobrightness and mechanical property. Further, while both focused- and plane-wave tracked log(VoA) values were artifactually inflated by mechanical reflections at inclusion boundaries, plane wave-tracked log(VoA) was more strongly impacted by off-axis scattering. Applied to three excised human cadaveric carotid plaques with spatially aligned histological validation, both log(VoA) methods detected regions of lipid, collagen, and calcium (CAL) deposits. These findings support that plane wave tracking performs comparably to focused tracking for log(VoA) imaging and that plane wave-tracked log(VoA) is a viable approach to discriminating clinically relevant atherosclerotic plaque features at a 30-fold higher framerate than by focused tracking.
Collapse
|
5
|
Lin Y, Huang J, Xu W, Cui C, Xu W, Li Z. Method for Carotid Artery 3-D Ultrasound Image Segmentation Based on CSWin Transformer. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:645-656. [PMID: 36460566 DOI: 10.1016/j.ultrasmedbio.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 10/22/2022] [Accepted: 11/06/2022] [Indexed: 06/17/2023]
Abstract
Precise segmentation of carotid artery (CA) structure is an important prerequisite for the medical assessment and detection of carotid plaques. For automatic segmentation of the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) in 3-D ultrasound images of the CA, a U-shaped CSWin transformer (U-CSWT) is proposed. Both the encoder and decoder of the U-CSWT are composed of hierarchical CSWT modules, which can capture rich global context information in the 3-D image. Experiments were performed on a 3-D ultrasound image data set of the CA, and the results indicate that the U-CSWT performs better than other convolutional neural network (CNN)-based and CNN-transformer hybrid methods. The model yields Dice coefficients of 94.6 ± 3.0% and 90.8 ± 5.1% for the MAB and LIB in the common carotid artery (CCA) and 92.9 ± 4.9% and 89.6 ± 6.2% for MAB and LIB in the bifurcation, respectively. Our U-CSWT is expected to become an effective method for automatic segmentation of 3-D ultrasound images of CA.
Collapse
Affiliation(s)
- Yanping Lin
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jianhua Huang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wangjie Xu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Cancan Cui
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenzhe Xu
- Department of Ultrasound, Zibo Central Hospital, Zibo, Shangdong Province, China
| | - Zhaojun Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Department of Ultrasound, Shanghai General Hospital Jiading Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.
| |
Collapse
|
6
|
Qian C, Su E, Ni X. Learning-based initialization for correntropy-based level sets to segment atherosclerotic plaque in ultrasound images. ULTRASONICS 2023; 127:106826. [PMID: 36058188 DOI: 10.1016/j.ultras.2022.106826] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/05/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
Carotid artery atherosclerosis is a significant cause of stroke. Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. Therefore, segmenting the atherosclerotic carotid plaque in an ultrasound image is an important task. Accurate plaque segmentation is helpful for the measurement of carotid plaque burden. This study proposes an automatic method for atherosclerotic plaque segmentation by using correntropy-based level sets (CLS) with learning-based initialization. We introduce the CLS model, containing the point-based local bias-field corrected image fitting method and correntropy-based distance measurement, to overcome the limitations of the ultrasound images. A supervised learning algorithm is employed to solve the automatic initialization problem of the variational methods. The proposed atherosclerotic plaque segmentation method is validated on 29 carotid ultrasound images, obtaining a Dice ratio of 90.6 ± 1.9% and an overlap index of 83.6 ± 3.2%. Moreover, by comparing the standard deviation of each evaluation index, it can be found that the proposed method is more robust for segmenting the atherosclerotic plaque. Our work shows that our proposed method can be more helpful than other variational models for measuring the carotid plaque burden.
Collapse
Affiliation(s)
- Chunjun Qian
- The Affiliated Changzhou NO.2 People's Hospital, Nanjing Medical University, Changhzou, Jiangsu, 213004, China; School of Electrical and Information Engineering, Changzhou Institute of Technology, Changzhou, Jiangsu, 213032, China
| | - Enjie Su
- Chinese Medical Hospital of Wujin, Changzhou, Jiangsu, 213100, China
| | - Xinye Ni
- The Affiliated Changzhou NO.2 People's Hospital, Nanjing Medical University, Changhzou, Jiangsu, 213004, China.
| |
Collapse
|
7
|
Yuan Y, Li C, Xu L, Zhu S, Hua Y, Zhang J. CSM-Net: Automatic joint segmentation of intima-media complex and lumen in carotid artery ultrasound images. Comput Biol Med 2022; 150:106119. [PMID: 37859275 DOI: 10.1016/j.compbiomed.2022.106119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/25/2022] [Accepted: 09/17/2022] [Indexed: 11/18/2022]
Abstract
The intima-media thickness (IMT) is an effective biomarker for atherosclerosis, which is commonly measured by ultrasound technique. However, the intima-media complex (IMC) segmentation for the IMT is challenging due to confused IMC boundaries and various noises. In this paper, we propose a flexible method CSM-Net for the joint segmentation of IMC and Lumen in carotid ultrasound images. Firstly, the cascaded dilated convolutions combined with the squeeze-excitation module are introduced for exploiting more contextual features on the highest-level layer of the encoder. Furthermore, a triple spatial attention module is utilized for emphasizing serviceable features on each decoder layer. Besides, a multi-scale weighted hybrid loss function is employed to resolve the class-imbalance issues. The experiments are conducted on a private dataset of 100 images for IMC and Lumen segmentation, as well as on two public datasets of 1600 images for IMC segmentation. For the private dataset, our method obtain the IMC Dice, Lumen Dice, Precision, Recall, and F1 score of 0.814 ± 0.061, 0.941 ± 0.024, 0.911 ± 0.044, 0.916 ± 0.039, and 0.913 ± 0.027, respectively. For the public datasets, we obtain the IMC Dice, Precision, Recall, and F1 score of 0.885 ± 0.067, 0.885 ± 0.070, 0.894 ± 0.089, and 0.885 ± 0.067, respectively. The results demonstrate that the proposed method precedes some cutting-edge methods, and the ablation experiments show the validity of each module. The proposed method may be useful for the IMC segmentation of carotid ultrasound images in the clinic. Our code is publicly available at https://github.com/yuanyc798/US-IMC-code.
Collapse
Affiliation(s)
- Yanchao Yuan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Cancheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Lu Xu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Shangming Zhu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yang Hua
- Department of Vascular Ultrasonography, XuanWu Hospital, Capital Medical University, Beijing, China; Beijing Diagnostic Center of Vascular Ultrasound, Beijing, China; Center of Vascular Ultrasonography, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China.
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.
| |
Collapse
|
8
|
Kaszczewski P, Elwertowski M, Leszczyński J, Ostrowski T, Gałązka Z. Volumetric Flow Assessment in Doppler Ultrasonography in Risk Stratification of Patients with Internal Carotid Stenosis and Occlusion. J Clin Med 2022; 11:jcm11030531. [PMID: 35159983 PMCID: PMC8836482 DOI: 10.3390/jcm11030531] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/27/2021] [Accepted: 01/17/2022] [Indexed: 01/27/2023] Open
Abstract
(1) Background: Alterations of blood flow volume in extracranial arteries may be related to the risk of occurrence of neurological symptoms. The aim of this study was the estimation of cerebral blood flow (CBF) in Doppler ultrasonography, as well as comparison of the flow volume in asymptomatic patients over 65 years old with ≥50%, and symptomatic patients with ≥70% internal carotid artery (ICA) stenosis, in order to assess whether the changes in the CBF correlates with the presence of neurological symptoms. (2) Methods: 308 patients over 65 years old were included in the retrospective cohort observational study: 154 asymptomatic with ≥50% ICA stenosis, 123 healthy volunteers, and 31 symptomatic referred for surgical treatment. The study group was split according to ICA stenosis (50–69%, 70–99% and occlusion). In all patients an extensive Doppler ultrasound examination with measurements of flow volume in common, internal, external carotid (ECA) and vertebral arteries (VA) was performed. (3) Results: Among asymptomatic (A) and symptomatic (S) patients with carotid stenosis 3 subgroups were identified: 57/154—37% (A) and 8/31—25.5% (S)—with significantly increased flow volume (CBF higher than reference range: average CBF + std. dev in the group of healthy volunteers), 67/154—43.5% (A) and 12/31—39% (S)—with similar to reference group flow volume (CBF within range average ± std.dev), and 30/154—19.5% (A) and 11/31—35.5% (S)—with decreased flow volume in extracranial arteries (flow lower than average-std.dev. in healthy volunteers). In symptomatic patients the percentage of patients with significant compensatory increased flow tends to raise with the severity of the stenosis, while simultaneous decline of number of patients with mild compensation (unchanged total CBF) is observed. The percentage of patients without compensation remains unchanged. In the group referred for surgical treatment (symptomatic, ≥70% ICA stenosis) the percentage of patients with flow compensation is twice as low as in the asymptomatic ones with similar degree of the ICA stenosis (8/31—25.8% vs. 26/53—49%, p = 0.04). Compensatory elevated flow was observed most frequently in ECA. (4) Conclusions: The presence of significant volumetric flow compensation has protective influence on developing ischaemic symptoms, including TIA or stroke. The assessment of cerebral inflow in Doppler ultrasonography may provide novel and easily accessible tool of identifying patients prone to cerebral ischaemia. The multivessel character of compensation with enhanced role of ECA justifies the importance of including this artery in the estimation of CBF.
Collapse
Affiliation(s)
- Piotr Kaszczewski
- Correspondence: (P.K.); (J.L.); Tel.: +48-22-599-25-54 (P.K. & J.L.)
| | | | - Jerzy Leszczyński
- Correspondence: (P.K.); (J.L.); Tel.: +48-22-599-25-54 (P.K. & J.L.)
| | | | | |
Collapse
|
9
|
Jiang M, Zhao Y, Chiu B. Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss. Med Phys 2021; 48:5096-5114. [PMID: 34309866 DOI: 10.1002/mp.15127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT) measured from three-dimensional (3D) ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of segmenting the CCA only. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification. METHODS Segmentation for CCA and ICA was performed independently using the proposed two-channel U-Net, which was driven by a novel loss function known as the adaptive triple Dice loss (ADTL) function. The training set was augmented by interpolating manual segmentation along the longitudinal direction, thereby taking continuity of the artery into account. A test-time augmentation (TTA) approach was applied, in which segmentation was performed three times based on the input axial images and its flipped versions; the final segmentation was generated by pixel-wise majority voting. RESULTS Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.1% ± 4.1% and 91.6% ± 6.6% for the MAB and LIB, in the CCA, respectively, and 94.2% ± 3.3% and 89.0% ± 8.1% for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently contributed to a statistically significant improvement to all boundaries except the LIB in ICA. CONCLUSIONS The proposed two-channel U-Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.
Collapse
Affiliation(s)
- Mingjie Jiang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, Hong Kong
| | - Yuan Zhao
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, Hong Kong
| | - Bernard Chiu
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, Hong Kong
| |
Collapse
|
10
|
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
|
11
|
Zhou R, Guo F, Azarpazhooh MR, Spence JD, Ukwatta E, Ding M, Fenster A. A Voxel-Based Fully Convolution Network and Continuous Max-Flow for Carotid Vessel-Wall-Volume Segmentation From 3D Ultrasound Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2844-2855. [PMID: 32142426 DOI: 10.1109/tmi.2020.2975231] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Vessel-wall-volume (VWV) is an important three-dimensional ultrasound (3DUS) metric used in the assessment of carotid plaque burden and monitoring changes in carotid atherosclerosis in response to medical treatment. To generate the VWV measurement, we proposed an approach that combined a voxel-based fully convolution network (Voxel-FCN) and a continuous max-flow module to automatically segment the carotid media-adventitia (MAB) and lumen-intima boundaries (LIB) from 3DUS images. Voxel-FCN includes an encoder consisting of a general 3D CNN and a 3D pyramid pooling module to extract spatial and contextual information, and a decoder using a concatenating module with an attention mechanism to fuse multi-level features extracted by the encoder. A continuous max-flow algorithm is used to improve the coarse segmentation provided by the Voxel-FCN. Using 1007 3DUS images, our approach yielded a Dice-similarity-coefficient (DSC) of 93.2±3.0% for the MAB in the common carotid artery (CCA), and 91.9±5.0% in the bifurcation by comparing algorithm and expert manual segmentations. We achieved a DSC of 89.5±6.7% and 89.3±6.8% for the LIB in the CCA and the bifurcation respectively. The mean errors between the algorithm-and manually-generated VWVs were 0.2±51.2 mm3 for the CCA and -4.0±98.2 mm3 for the bifurcation. The algorithm segmentation accuracy was comparable to intra-observer manual segmentation but our approach required less than 1s, which will not alter the clinical work-flow as 10s is required to image one side of the neck. Therefore, we believe that the proposed method could be used clinically for generating VWV to monitor progression and regression of carotid plaques.
Collapse
|
12
|
Zhu G, Hom J, Li Y, Jiang B, Rodriguez F, Fleischmann D, Saloner D, Porcu M, Zhang Y, Saba L, Wintermark M. Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease. Cardiovasc Diagn Ther 2020; 10:1048-1067. [PMID: 32968660 DOI: 10.21037/cdt.2020.03.10] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Carotid artery plaque is a measure of atherosclerosis and is associated with future risk of atherosclerotic cardiovascular disease (ASCVD), which encompasses coronary, cerebrovascular, and peripheral arterial diseases. With advanced imaging techniques, computerized tomography (CT) and magnetic resonance imaging (MRI) have shown their potential superiority to routine ultrasound to detect features of carotid plaque vulnerability, such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC), and calcification. The correlation between imaging features and histological changes of carotid plaques has been investigated. Imaging of carotid features has been used to predict the risk of cardiovascular events. Other techniques such as nuclear imaging and intra-vascular ultrasound (IVUS) have also been proposed to better understand the vulnerable carotid plaque features. In this article, we review the studies of imaging specific carotid plaque components and their correlation with risk scores.
Collapse
Affiliation(s)
- Guangming Zhu
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jason Hom
- Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ying Li
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA.,Clinical Medical Research Center, Luye Pharma Group Ltd., Beijing 100000, China
| | - Bin Jiang
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Stanford University, Palo Alto, CA, USA
| | - Dominik Fleischmann
- Department of Radiology, Cardiovascular Imaging Section, Stanford University School of Medicine, Palo Alto, CA, USA
| | - David Saloner
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Michele Porcu
- Dipartimento di Radiologia, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy
| | - Yanrong Zhang
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Luca Saba
- Dipartimento di Radiologia, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy
| | - Max Wintermark
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA
| |
Collapse
|
13
|
Azzopardi C, Camilleri KP, Hicks YA. Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks. IEEE J Biomed Health Inform 2020; 24:1004-1015. [PMID: 31944969 DOI: 10.1109/jbhi.2020.2965088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source.
Collapse
|
14
|
Song S, Heo R, Lee SE, Park J, Lee J, Kim S, Cho IJ, Chang HJ. Comparing the feasibility and accuracy of three-dimensional ultrasound to two-dimensional ultrasound and computed tomography angiography in the assessment of carotid atherosclerosis. Echocardiography 2019; 36:2241-2250. [PMID: 31742790 DOI: 10.1111/echo.14543] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 09/29/2019] [Accepted: 10/22/2019] [Indexed: 12/01/2022] Open
Abstract
AIMS Two-dimensional ultrasound (2D-US) is the mainstay imaging technique used to evaluate carotid atherosclerosis. An automated single sweep three-dimensional ultrasound (3D-US) technique became available. We evaluated the feasibility and accuracy of 3D-US in the assessment of carotid plaques compared to those of 2D-US. Carotid computed tomography angiography (CTA) was used as a reference. METHODS AND RESULTS Among 126 stroke patients who underwent carotid 2D-US, 73 underwent 3D-US and carotid CTA. 3D-US was pursued when there were carotid plaques or when area stenosis was ≥ 20% by 2D-US. Both 2D- and 3D-US images of the carotid arteries were acquired using a dedicated ultrasound system that was equipped with the single sweep volumetric transducer. In total, 266 arteries from 73 patients were selected for comparison of the detection rate of carotid plaques between 2D- and 3D-US. Among the 73 patients, carotid CTA detected 139 plaques. 3D-US demonstrated a higher detection rate of carotid plaques than did 2D-US (108 plaques (77.9%) vs. 70 plaques (50.4%)) when using carotid CTA as a reference standard. Carotid plaque volume (PV) of 133 vessels from 73 patients were quantitatively evaluated using both 3D-US and carotid CTA. Plaque volume of carotid artery was comparable between 3D-US and CTA (148.5 ± 133.0 mm3 vs. 154.1 ± 134.6 mm3 , P = .998, R: 0.9825, P-value for r < .001). CONCLUSION 3D-US using a single sweep technique was a feasible and accurate method of detecting arterial plaques and assessing plaque volume.
Collapse
Affiliation(s)
- Shinjeong Song
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.,Yonsei-Cedars Sinai Integrative Cardiovascular Imaging Research Centre, Yonsei University, Seoul, South Korea
| | - Ran Heo
- Yonsei-Cedars Sinai Integrative Cardiovascular Imaging Research Centre, Yonsei University, Seoul, South Korea.,Division of Cardiology, Department of Internal Medicine, Hanyang University Medical Centre, Seoul, Korea
| | - Sang-Eun Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.,Yonsei-Cedars Sinai Integrative Cardiovascular Imaging Research Centre, Yonsei University, Seoul, South Korea
| | - Jinki Park
- Medical Imaging Research Group, Samsung Medison, Seoul, Korea
| | - Jinyong Lee
- Medical Imaging Research Group, Samsung Medison, Seoul, Korea
| | - Sujin Kim
- Yonsei-Cedars Sinai Integrative Cardiovascular Imaging Research Centre, Yonsei University, Seoul, South Korea
| | - In Jeong Cho
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.,Yonsei-Cedars Sinai Integrative Cardiovascular Imaging Research Centre, Yonsei University, Seoul, South Korea
| | - Hyuk-Jae Chang
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.,Yonsei-Cedars Sinai Integrative Cardiovascular Imaging Research Centre, Yonsei University, Seoul, South Korea.,Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
15
|
Application of fractal theory and fuzzy enhancement in ultrasound image segmentation. Med Biol Eng Comput 2018; 57:623-632. [DOI: 10.1007/s11517-018-1907-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 09/26/2018] [Indexed: 01/08/2023]
|
16
|
Cheng J, Chen Y, Yu Y, Chiu B. Carotid plaque segmentation from three-dimensional ultrasound images by direct three-dimensional sparse field level-set optimization. Comput Biol Med 2018; 94:27-40. [PMID: 29407996 DOI: 10.1016/j.compbiomed.2018.01.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/08/2018] [Accepted: 01/08/2018] [Indexed: 10/18/2022]
Abstract
Total plaque volume (TPV) measured from 3D carotid ultrasound has been shown to be able to predict cardiovascular events and is sensitive in detecting treatment effects. Manual plaque segmentation was performed in previous studies to quantify TPV, but is tedious, requires long training times and is prone to observer variability. This article introduces the first 3D direct volume-based level-set algorithm to segment plaques from 3D carotid ultrasound images. The plaque surfaces were first initialized based on the lumen and outer wall boundaries generated by a previously described semi-automatic algorithm and then deformed by a direct three-dimensional sparse field level-set algorithm, which enforced the longitudinal continuity of the segmented plaque surfaces. This is a marked advantage as compared to a previously proposed 2D slice-by-slice plaque segmentation method. In plaque boundary initialization, the previous technique performed a search on lines connecting corresponding point pairs of the outer wall and lumen boundaries. A limitation of this initialization strategy was that an inaccurate initial plaque boundary would be generated if the plaque was not enclosed entirely by the wall and lumen boundaries. A mechanism is proposed to extend the search range in order to capture the entire plaque if the outer wall boundary lies on a weak edge in the 3D ultrasound image. The proposed method was compared with the previously described 2D slice-by-slice plaque segmentation method in 26 three-dimensional carotid ultrasound images containing 27 plaques with volumes ranging from 12.5 to 450.0 mm3. The manually segmented plaque boundaries serve as the surrogate gold standard. Segmentation accuracy was quantified by volume-, area- and distance-based metrics, including absolute plaque volume difference (|ΔPV|), Dice similarity coefficient (DSC), mean and maximum absolute distance (MAD and MAXD). The proposed direct 3D plaque segmentation algorithm was associated with a significantly lower |ΔPV|, MAD and MAXD, and a significantly higher DSC compared to the previously described slice-by-slice algorithm (|ΔPV|:p=0.012, DSC: p=2.1×10-4, MAD: p=1.3×10-4, MAXD: p=5.2×10-4). The proposed 3D volume-based algorithm required 72±22 s to segment a plaque, which is 40% lower than the 2D slice-by-slice algorithm (114±18 s). The proposed automatic plaque segmentation method generates accurate and reproducible boundaries efficiently and will allow for streamlining plaque quantification based on 3D ultrasound images.
Collapse
Affiliation(s)
- Jieyu Cheng
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong
| | - Yimin Chen
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong
| | - Yanyan Yu
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong
| | - Bernard Chiu
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong.
| |
Collapse
|
17
|
Calogero E, Fabiani I, Pugliese NR, Santini V, Ghiadoni L, Di Stefano R, Galetta F, Sartucci F, Penno G, Berchiolli R, Ferrari M, Cioni D, Napoli V, De Caterina R, Di Bello V, Caramella D. Three-Dimensional Echographic Evaluation of Carotid Artery Disease. J Cardiovasc Echogr 2018; 28:218-227. [PMID: 30746325 PMCID: PMC6341847 DOI: 10.4103/jcecho.jcecho_57_18] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The introduction of three-dimensional echography (3D echo) in vascular field is not recent, but it still remains a seldom-used technique because of the costs of ultrasound probe and the need of dedicated laboratories. Therefore, despite significant prognostic implications, the high diagnostic accuracy in plaque definition, and the relative ease of use, 3D echo in vascular field is a niche technique. The purpose of this review is mainly clinical and intends to demonstrate the potential strength of a 3D approach, including technical aspects, in order to present to clinicians and imagers the appealing aspects of a noninvasive and radiation-free methodology with relevant diagnostic and prognostic correlates in the assessment of carotid atherosclerosis. A comprehensive literature search (since 1990s to date) using the PubMed, MEDLINE, and Cochrane libraries databases has been conducted. Articles written in English have been assessed, including reviews, clinical trials, meta-analyses, and interventional/observational studies. Manual cross-referencing was also performed, and relevant references from selected articles were reviewed. The search was limited to studies conducted in humans. Search terms, retrieved also with PubMed Advanced search and AND/OR Boolean operators (mainly in title and abstract), included three-dimensional, echo, stroke/transient ischemic attack, predictors, carotid, imaging, and biomarkers.
Collapse
Affiliation(s)
- Enrico Calogero
- Department of Medical, Surgical, Molecular and Critical Area Pathology, Pisa University, Pisa, Italy.,Department of Cardiac, Thoracic and Vascular, Pisa University, Pisa, Italy
| | - Iacopo Fabiani
- Department of Medical, Surgical, Molecular and Critical Area Pathology, Pisa University, Pisa, Italy.,Department of Cardiac, Thoracic and Vascular, Pisa University, Pisa, Italy
| | - Nicola Riccardo Pugliese
- Department of Medical, Surgical, Molecular and Critical Area Pathology, Pisa University, Pisa, Italy.,Department of Cardiac, Thoracic and Vascular, Pisa University, Pisa, Italy
| | - Veronica Santini
- Department of Clinical and Experimental Medicine, Pisa University, Pisa, Italy
| | - Lorenzo Ghiadoni
- Department of Clinical and Experimental Medicine, Pisa University, Pisa, Italy
| | - Rossella Di Stefano
- Department of Medical, Surgical, Molecular and Critical Area Pathology, Pisa University, Pisa, Italy.,Department of Cardiac, Thoracic and Vascular, Pisa University, Pisa, Italy
| | - Fabio Galetta
- Department of Clinical and Experimental Medicine, Pisa University, Pisa, Italy
| | - Ferdinando Sartucci
- Department of Clinical and Experimental Medicine, Pisa University, Pisa, Italy
| | - Giuseppe Penno
- Department of Clinical and Experimental Medicine, Pisa University, Pisa, Italy
| | - Raffaella Berchiolli
- Department of Translational Research and New Technologies in Medicine and Surgery, Pisa University, Pisa, Italy
| | - Mauro Ferrari
- Department of Translational Research and New Technologies in Medicine and Surgery, Pisa University, Pisa, Italy
| | - Dania Cioni
- Department of Translational Research and New Technologies in Medicine and Surgery, Pisa University, Pisa, Italy
| | - Vinicio Napoli
- Department of Translational Research and New Technologies in Medicine and Surgery, Pisa University, Pisa, Italy
| | - Raffaele De Caterina
- Department of Medical, Surgical, Molecular and Critical Area Pathology, Pisa University, Pisa, Italy.,Department of Cardiac, Thoracic and Vascular, Pisa University, Pisa, Italy
| | - Vitantonio Di Bello
- Department of Medical, Surgical, Molecular and Critical Area Pathology, Pisa University, Pisa, Italy.,Department of Cardiac, Thoracic and Vascular, Pisa University, Pisa, Italy
| | - Davide Caramella
- Department of Translational Research and New Technologies in Medicine and Surgery, Pisa University, Pisa, Italy
| |
Collapse
|
18
|
Cires-Drouet RS, Mozafarian M, Ali A, Sikdar S, Lal BK. Imaging of high-risk carotid plaques: ultrasound. Semin Vasc Surg 2017; 30:44-53. [PMID: 28818258 DOI: 10.1053/j.semvascsurg.2017.04.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Duplex ultrasonography has a well-established role in the assessment of the degree of stenosis caused by carotid atherosclerosis. This assessment is derived from Doppler velocity changes induced by the narrowing lumen of the artery. New research into the mechanisms for plaque rupture and atheroembolic stroke indicates that the degree of narrowing is an imperfect predictor of stroke risk, and that other factors, such as plaque composition and remodeling and biomechanical forces acting on the plaque, can play a role. New advances in ultrasound imaging technology have made it possible to investigate these measures of plaque vulnerability to identify pre-embolic unstable carotid plaques. Efforts have been made to quantify the morphologic appearance of the plaque in B-mode images and to correlate them with histology. Additional research has resulted in the first generation of clinically available 3-dimensional ultrasound transducers that reduce operator-dependence and variability. Finally, ultrasonography provides real-time imaging and physiologic information that can be utilized to measure disruptive forces acting on carotid plaques. We review some of these exciting developments in ultrasonography and discuss how these may impact clinical practice.
Collapse
Affiliation(s)
- Rafael S Cires-Drouet
- Center for Vascular Diagnostics, Division of Vascular Surgery, University of Maryland School of Medicine, 22 South Greene Street, S10-B00, Baltimore, MD 21201
| | - Mahvash Mozafarian
- Center for Vascular Diagnostics, Division of Vascular Surgery, University of Maryland School of Medicine, 22 South Greene Street, S10-B00, Baltimore, MD 21201
| | - Amir Ali
- Center for Vascular Diagnostics, Division of Vascular Surgery, University of Maryland School of Medicine, 22 South Greene Street, S10-B00, Baltimore, MD 21201; Department of Bioengineering, George Mason University, Fairfax, VA
| | | | - Brajesh K Lal
- Center for Vascular Diagnostics, Division of Vascular Surgery, University of Maryland School of Medicine, 22 South Greene Street, S10-B00, Baltimore, MD 21201; Vascular Service, Veterans Affairs Medical Center, Baltimore, MD.
| |
Collapse
|
19
|
Semiautomatic quantification of carotid plaque volume with three-dimensional ultrasound imaging. J Vasc Surg 2017; 65:1407-1417. [PMID: 28274755 DOI: 10.1016/j.jvs.2016.11.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 11/14/2016] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Vessel wall volume (VWV) assessed by three-dimensional duplex ultrasound (3DUS) imaging provides a more comprehensive measure of plaque burden than conventional two-dimensional measures of diameter stenosis. We previously demonstrated that manual outlining of the arterial lumen-intima boundary and outer wall boundary can be performed reliably on images obtained with a commercially available 3D-DUS transducer. Manual segmentation, however, is time consuming (∼45 minutes), limiting its clinical translation. We have developed a semiautomatic algorithm (manual selection of the carotid bifurcation image with subsequent automatic plaque outlining) to outline carotid plaques on 3DUS data sets. In this study, we investigated the accuracy, reproducibility, reliability, and time taken by this algorithm. METHODS 3DUS data sets from 30 patients with asymptomatic ≥50% carotid stenosis underwent manual outlining of lumen-intima boundary and outer wall boundary to measure VWV. Two observers implemented a semiautomatic segmentation algorithm. The algorithm's accuracy was compared with manual outlining using the Pearson correlation coefficient. The Dice similarity coefficient (DSC) and modified-Hausdorff distance (MHD) were used to quantify the geometric similarity of the outlines. We also compared results after an intermediate stage of the algorithm vs the complete algorithm. Reproducibility and the least amount of detectable change in plaque volume were computed for each method. Intraobserver and interobserver metrics for each method were computed using the intraclass correlation coefficient (ICC), coefficient of variability (CV), minimum detectable change (MDC), and standard error of measurement (SEM) of the VWV. RESULTS Plaque volume estimates obtained from the semiautomatic algorithm were accurate compared with manual outlining. The Pearson correlation coefficient was 0.76 (P < .001), and measurements were geometrically similar (DSC, 0.85; MHD, 0.48 mm). The algorithm was more reproducible and reliable and could detect smaller changes in plaque volume on repeat imaging (low interobserver variability: ICC, 0.9; CV, 8.22%; MDC, 5.57%; SEM, 1.45%; DSC, 0.88; MHD, 0.43 mm). Intraobserver variability was even lower (ICC, 0.9; CV, 8%; MDC, 3.62%; SEM, 1.31%; DSC, 0.89; MHD, 0.37 mm). Plaque volume estimates at the intermediate stage of the algorithm matched results from the full algorithm (Pearson correlation coefficient, 0.76; DSC, 0.84; MHD, 0.52 mm). The intermediate approach, however, was less reliable than the full algorithm (interobserver: ICC, 0.81; CV, 11.7%; MDC, 9.58%; SEM, 3.46%; DSC, 0.88; MHD, 0.42 mm; intraobserver: ICC, 0.87; CV, 8.6%; MDC, 4.55%; SEM, 1.64%; DSC, 0.89; MHD, 0.38 mm). The full algorithm required ∼14 minutes to implement. However, a quick (7 minutes) and accurate assessment of VWV can be obtained by running only the intermediate stage of the algorithm, although with a loss in repeatability and reliability. CONCLUSIONS We present a unique algorithm to perform semiautomatic quantification of carotid plaque volume using 3DUS imaging. It is quick (mean time, 14 minutes), accurate, repeatable, and implementable in a clinical environment and in longitudinal studies tracking plaque progression. It reliably detects plaque volume changes as low as 4% to 6% with 95% confidence.
Collapse
|
20
|
Chung SW, Shih CC, Huang CC. Freehand three-dimensional ultrasound imaging of carotid artery using motion tracking technology. ULTRASONICS 2017; 74:11-20. [PMID: 27721196 DOI: 10.1016/j.ultras.2016.09.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Revised: 09/13/2016] [Accepted: 09/26/2016] [Indexed: 05/22/2023]
Abstract
Ultrasound imaging has been extensively used for determining the severity of carotid atherosclerotic stenosis. In particular, the morphological characterization of carotid plaques can be performed for risk stratification of patients. However, using 2D ultrasound imaging for detecting morphological changes in plaques has several limitations. Due to the scan was performed on a single longitudinal cross-section, the selected 2D image is difficult to represent the entire morphology and volume of plaque and vessel lumen. In addition, the precise positions of 2D ultrasound images highly depend on the radiologists' experience, it makes the serial long-term exams of anti-atherosclerotic therapies are difficult to relocate the same corresponding planes by using 2D B-mode images. This has led to the recent development of three-dimensional (3D) ultrasound imaging, which offers improved visualization and quantification of complex morphologies of carotid plaques. In the present study, a freehand 3D ultrasound imaging technique based on optical motion tracking technology is proposed. Unlike other optical tracking systems, the marker is a small rigid body that is attached to the ultrasound probe and is tracked by eight high-performance digital cameras. The probe positions in 3D space coordinates are then calibrated at spatial and temporal resolutions of 10μm and 0.01s, respectively. The image segmentation procedure involves Otsu's and the active contour model algorithms and accurately detects the contours of the carotid arteries. The proposed imaging technique was verified using normal artery and atherosclerotic stenosis phantoms. Human experiments involving freehand scanning of the carotid artery of a volunteer were also performed. The results indicated that compared with manual segmentation, the lowest percentage errors of the proposed segmentation procedure were 7.8% and 9.1% for the external and internal carotid arteries, respectively. Finally, the effect of handshaking was calibrated using the optical tracking system for reconstructing a 3D image.
Collapse
Affiliation(s)
- Shao-Wen Chung
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Cho-Chiang Shih
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Chung Huang
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
| |
Collapse
|