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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.
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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
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Jiang M, Chiu B. A Dual-Stream Centerline-Guided Network for Segmentation of the Common and Internal Carotid Arteries From 3D Ultrasound Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2690-2705. [PMID: 37015114 DOI: 10.1109/tmi.2023.3263537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Segmentation of the carotid section encompassing the common carotid artery (CCA), the bifurcation and the internal carotid artery (ICA) from three-dimensional ultrasound (3DUS) is required to measure the vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT), shown to be sensitive to treatment effect. We proposed an approach to combine a centerline extraction network (CHG-Net) and a dual-stream centerline-guided network (DSCG-Net) to segment the lumen-intima (LIB) and media-adventitia boundaries (MAB) from 3DUS images. Correct arterial location is essential for successful segmentation of the carotid section encompassing the bifurcation. We addressed this challenge by using the arterial centerline to enhance the localization accuracy of the segmentation network. The CHG-Net was developed to generate a heatmap indicating high probability regions for the centerline location, which was then integrated with the 3DUS image by the DSCG-Net to generate the MAB and LIB. The DSCG-Net includes a scale-based and a spatial attention mechanism to fuse multi-level features extracted by the encoder, and a centerline heatmap reconstruction side-branch connected to the end of the encoder to increase the generalization ability of the network. Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.8±1.9% and 92.3±5.4% for CCA MAB and LIB, respectively, and 93.2±4.4% and 89.0±10.0% for ICA MAB and LIB, respectively. Our approach outperformed four state-of-the-art 3D CNN models, even after their performances were boosted by centerline guidance. The efficiency afforded by the framework would allow it to be incorporated into the clinical workflow for improved quantification of plaque change.
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An adaptively weighted ensemble of multiple CNNs for carotid ultrasound image segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Zhou R, Guo F, Azarpazhooh MR, Spence JD, Gan H, Ding M, Fenster A. Carotid Vessel-Wall-Volume Ultrasound Measurement via a UNet++ Ensemble Algorithm Trained on Small Data Sets. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1031-1036. [PMID: 36642588 DOI: 10.1016/j.ultrasmedbio.2022.12.005] [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: 08/17/2022] [Revised: 11/02/2022] [Accepted: 12/10/2022] [Indexed: 06/17/2023]
Abstract
Vessel wall volume (VWV) is a 3-D ultrasound measurement for the assessment of therapy in patients with carotid atherosclerosis. Deep learning can be used to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) and to quantify VWV automatically; however, it typically requires large training data sets with expert manual segmentation, which are difficult to obtain. In this study, a UNet++ ensemble approach was developed for automated VWV measurement, trained on five small data sets (n = 30 participants) and tested on 100 participants with clinically diagnosed coronary artery disease enrolled in a multicenter CAIN trial. The Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Pearson correlation coefficient (r), Bland-Altman plots and coefficient of variation (CoV) were used to evaluate algorithm segmentation accuracy, agreement and reproducibility. The UNet++ ensemble yielded DSCs of 91.07%-91.56% and 87.53%-89.44% and ASSDs of 0.10-0.11 mm and 0.33-0.39 mm for the MAB and LIB, respectively; the algorithm VWV measurements were correlated (r = 0.763-0.795, p < 0.001) with manual segmentations, and the CoV for VWV was 8.89%. In addition, the UNet++ ensemble trained on 30 participants achieved a performance similar to that of U-Net and Voxel-FCN trained on 150 participants. These results suggest that our approach could provide accurate and reproducible carotid VWV measurements using relatively small training data sets, supporting deep learning applications for monitoring atherosclerosis progression in research and clinical trials.
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Affiliation(s)
- Ran Zhou
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei, China
| | - Fumin Guo
- Wuhan National Laboratory for Optoelectronics, Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - M Reza Azarpazhooh
- Stroke Prevention and Atherosclerosis Research Centre, Robarts Research Institute, Western University, London, Ontario, Canada
| | - J David Spence
- Stroke Prevention and Atherosclerosis Research Centre, Robarts Research Institute, Western University, London, Ontario, Canada; Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Haitao Gan
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei, China
| | - Mingyue Ding
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Aaron Fenster
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
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Chen X, Zhao Y, Spence JD, Chiu B. Quantification of Local Vessel Wall and Plaque Volume Change for Assessment of Effects of Therapies on Carotid Atherosclerosis Based on 3-D Ultrasound Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:773-786. [PMID: 36566092 DOI: 10.1016/j.ultrasmedbio.2022.10.017] [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: 05/02/2022] [Revised: 09/21/2022] [Accepted: 10/23/2022] [Indexed: 06/17/2023]
Abstract
We developed a new method to measure the voxel-based vessel-wall-plus-plaque volume (VWV). In addition to quantifying local thickness change as in the previously introduced vessel-wall-plus-plaque thickness (VWT) metric, voxel-based VWV further considers the circumferential change associated with vascular remodeling. Three-dimensional ultrasound images were acquired at baseline and 1 y afterward. The vessel wall region was divided into small voxels with the voxel-based VWV change (ΔVVol%) computed by taking the percentage volume difference between corresponding voxels in the baseline and follow-up images. A 3-D carotid atlas was developed to allow visualization of the local thickness and circumferential change patterns in the pomegranate versus the placebo groups. A new patient-based biomarker was obtained by computing the mean ΔVVol% over the entire 3-D map for each patient (ΔVVol%¯). ΔVVol%¯ detected a significant difference between patients randomized to pomegranate juice/extract and placebo groups (p = 0.0002). The number of patients required by ΔVVol%¯ to establish statistical significance was approximately a third of that required by the local VWT biomarker. The increased sensitivity afforded by the proposed biomarker improves the cost-effectiveness of clinical studies evaluating new anti-atherosclerotic treatments.
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Affiliation(s)
- Xueli Chen
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Yuan Zhao
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - J David Spence
- Stroke Prevention & Atherosclerosis Research Centre, Robarts Research Institute, London, Ontario, Canada
| | - Bernard Chiu
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China.
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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.
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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.
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Zhou R, Ou Y, Fang X, Azarpazhooh MR, Gan H, Ye Z, Spence JD, Xu X, Fenster A. Ultrasound carotid plaque segmentation via image reconstruction-based self-supervised learning with limited training labels. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1617-1636. [PMID: 36899501 DOI: 10.3934/mbe.2023074] [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: 06/18/2023]
Abstract
Carotid total plaque area (TPA) is an important contributing measurement to the evaluation of stroke risk. Deep learning provides an efficient method for ultrasound carotid plaque segmentation and TPA quantification. However, high performance of deep learning requires datasets with many labeled images for training, which is very labor-intensive. Thus, we propose an image reconstruction-based self-supervised learning algorithm (IR-SSL) for carotid plaque segmentation when few labeled images are available. IR-SSL consists of pre-trained and downstream segmentation tasks. The pre-trained task learns region-wise representations with local consistency by reconstructing plaque images from randomly partitioned and disordered images. The pre-trained model is then transferred to the segmentation network as the initial parameters in the downstream task. IR-SSL was implemented with two networks, UNet++ and U-Net, and evaluated on two independent datasets of 510 carotid ultrasound images from 144 subjects at SPARC (London, Canada) and 638 images from 479 subjects at Zhongnan hospital (Wuhan, China). Compared to the baseline networks, IR-SSL improved the segmentation performance when trained on few labeled images (n = 10, 30, 50 and 100 subjects). For 44 SPARC subjects, IR-SSL yielded Dice-similarity-coefficients (DSC) of 80.14-88.84%, and algorithm TPAs were strongly correlated (r=0.962-0.993, p < 0.001) with manual results. The models trained on the SPARC images but applied to the Zhongnan dataset without retraining achieved DSCs of 80.61-88.18% and strong correlation with manual segmentation (r=0.852-0.978, p < 0.001). These results suggest that IR-SSL could improve deep learning when trained on small labeled datasets, making it useful for monitoring carotid plaque progression/regression in clinical use and trials.
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Affiliation(s)
- Ran Zhou
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - Yanghan Ou
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - Xiaoyue Fang
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | | | - Haitao Gan
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - Zhiwei Ye
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - J David Spence
- Robarts Research Institute, Western University, London, Canada
| | - Xiangyang Xu
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Aaron Fenster
- Robarts Research Institute, Western University, London, Canada
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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.
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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.
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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]
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