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Liu M, Gao W, Song D, Dong Y, Hong S, Cui C, Shi S, Wu K, Chen J, Xu J, Dong F. A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images. Vascular 2024:17085381241246312. [PMID: 38656244 DOI: 10.1177/17085381241246312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
OBJECTIVES Assessment of plaque stenosis severity allows better management of carotid source of stroke. Our objective is to create a deep learning (DL) model to segment carotid intima-media thickness and plaque and further automatically calculate plaque stenosis severity on common carotid artery (CCA) transverse section ultrasound images. METHODS Three hundred and ninety images from 376 individuals were used to train (235/390, 60%), validate (39/390, 10%), and test (116/390, 30%) on a newly proposed CANet model. We also evaluated the model on an external test set of 115 individuals with 122 images acquired from another hospital. Comparative studies were conducted between our CANet model with four state-of-the-art DL models and two experienced sonographers to re-evaluate the present model's performance. RESULTS On the internal test set, our CANet model outperformed the four comparative models with Dice values of 95.22% versus 90.15%, 87.48%, 90.22%, and 91.56% on lumen-intima (LI) borders and 96.27% versus 91.40%, 88.94%, 91.19%, and 92.88% on media-adventitia (MA) borders. On the external test set, our model still produced excellent results with a Dice value of 92.41%. Good consistency of stenosis severity calculation was observed between CANet model and experienced sonographers, with Intraclass Correlation Coefficient (ICC) of 0.927 and 0.702, Pearson's Correlation Coefficient of 0.928 and 0.704 on internal and external test set, respectively. CONCLUSIONS Our CANet model achieved excellent performance in the segmentation of carotid IMT and plaques as well as automated calculation of stenosis severity.
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Affiliation(s)
- Mengmeng Liu
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, PR China
| | - Wenjing Gao
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, PR China
| | - Di Song
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, PR China
| | - Yinghui Dong
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, PR China
| | - Shaofu Hong
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, PR China
| | - Chen Cui
- Illuminate, LLC, Shenzhen, China
- Microport Prophecy, Shanghai, China
| | - Siyuan Shi
- Illuminate, LLC, Shenzhen, China
- Microport Prophecy, Shanghai, China
| | - Kai Wu
- Illuminate, LLC, Shenzhen, China
- Microport Prophecy, Shanghai, China
| | - Jiayi Chen
- Illuminate, LLC, Shenzhen, China
- Microport Prophecy, Shanghai, China
| | - Jinfeng Xu
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, PR China
| | - Fajin Dong
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen People's Hospital, Shenzhen, PR China
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Spence JD. Vessel Wall Volume and Plaque Volume Should Replace Carotid Intima-Media Thickness. Am J Hypertens 2024; 37:270-272. [PMID: 38198747 DOI: 10.1093/ajh/hpae004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/06/2024] [Indexed: 01/12/2024] Open
Affiliation(s)
- J David Spence
- Neurology & Clinical Pharmacology, Western University, London, Ontario, Canada
- Stroke Prevention & Atherosclerosis Research Centre, Robarts Research Institute, London, Ontario, Canada
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Wang Y, Zhu B, Kong L, Wang J, Gao B, Wang J, Tian D, Yao Y. BPSegSys: A Brachial Plexus Nerve Trunk Segmentation System Using Deep Learning. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:374-383. [PMID: 38176984 DOI: 10.1016/j.ultrasmedbio.2023.11.009] [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: 06/10/2023] [Revised: 11/13/2023] [Accepted: 11/16/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can be used to observe the target nerve and its surrounding structures, the puncture needle's advancement and local anesthetics spread in real time. The key in UGNB is nerve identification. With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently. METHODS We established a public data set containing 320 ultrasound images of brachial plexus (BP). Three experienced doctors jointly produced the BP segmentation ground truth and labeled brachial plexus trunks. We designed a brachial plexus segmentation system (BPSegSys) based on deep learning. RESULTS BPSegSys achieves experienced-doctor-level nerve identification performance in various experiments. We evaluated BPSegSys performance in terms of intersection-over-union (IoU). Considering three data set groups in our established public data set, the IoUs of BPSegSys were 0.5350, 0.4763 and 0.5043, respectively, which exceed the IoUs 0.5205, 0.4704 and 0.4979 of experienced doctors. In addition, we determined that BPSegSys can help doctors identify brachial plexus trunks more accurately, with IoU improvement up to 27%, which has significant clinical application value. CONCLUSION We establish a data set for brachial plexus trunk identification and designed a BPSegSys to identify the brachial plexus trunks. BPSegSys achieves the doctor-level identification of the brachial plexus trunks and improves the accuracy and efficiency of doctors' identification of the brachial plexus trunks.
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Affiliation(s)
- Yu Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Binbin Zhu
- Department of Anesthesiology, Affiliated Hospital of Medical School of Ningbo University, Ningbo University, China
| | - Lingsi Kong
- Department of Anesthesiology, Ningbo No. 6 Hospital, Ningbo, China
| | - Jianlin Wang
- Department of Anesthesiology, Ningbo No. 6 Hospital, Ningbo, China
| | - Bin Gao
- Department of Anesthesiology, Affiliated Hospital of Medical School of Ningbo University, Ningbo University, China
| | - Jianhua Wang
- Department of Anesthesiology, Affiliated Hospital of Medical School of Ningbo University, Ningbo University, China
| | - Dingcheng Tian
- Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA.
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Chen L, Zeng B, Shen J, Xu J, Cai Z, Su S, Chen J, Cai X, Ying T, Hu B, Wu M, Chen X, Zheng Y. Bone age assessment based on three-dimensional ultrasound and artificial intelligence compared with paediatrician-read radiographic bone age: protocol for a prospective, diagnostic accuracy study. BMJ Open 2024; 14:e079969. [PMID: 38401893 PMCID: PMC10895244 DOI: 10.1136/bmjopen-2023-079969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/31/2024] [Indexed: 02/26/2024] Open
Abstract
INTRODUCTION Radiographic bone age (BA) assessment is widely used to evaluate children's growth disorders and predict their future height. Moreover, children are more sensitive and vulnerable to X-ray radiation exposure than adults. The purpose of this study is to develop a new, safer, radiation-free BA assessment method for children by using three-dimensional ultrasound (3D-US) and artificial intelligence (AI), and to test the diagnostic accuracy and reliability of this method. METHODS AND ANALYSIS This is a prospective, observational study. All participants will be recruited through Paediatric Growth and Development Clinic. All participants will receive left hand 3D-US and X-ray examination at the Shanghai Sixth People's Hospital on the same day, all images will be recorded. These image related data will be collected and randomly divided into training set (80% of all) and test set (20% of all). The training set will be used to establish a cascade network of 3D-US skeletal image segmentation and BA prediction model to achieve end-to-end prediction of image to BA. The test set will be used to evaluate the accuracy of AI BA model of 3D-US. We have developed a new ultrasonic scanning device, which can be proposed to automatic 3D-US scanning of hands. AI algorithms, such as convolutional neural network, will be used to identify and segment the skeletal structures in the hand 3D-US images. We will achieve automatic segmentation of hand skeletal 3D-US images, establish BA prediction model of 3D-US, and test the accuracy of the prediction model. ETHICS AND DISSEMINATION The Ethics Committee of Shanghai Sixth People's Hospital approved this study. The approval number is 2022-019. A written informed consent will be obtained from their parent or guardian of each participant. Final results will be published in peer-reviewed journals and presented at national and international conferences. TRIAL REGISTRATION NUMBER ChiCTR2200057236.
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Affiliation(s)
- Li Chen
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bolun Zeng
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Shen
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiangchang Xu
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zehang Cai
- Shantou Institute of Ultrasonic Instruments Co., Ltd, Shantou, China
| | - Shudian Su
- Shantou Institute of Ultrasonic Instruments Co., Ltd, Shantou, China
| | - Jie Chen
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojun Cai
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Ying
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Hu
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Wu
- Department of Pediatrics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanyi Zheng
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Ding J, Zhou R, Fang X, Wang F, Wang J, Gan H, Fenster A. An image registration-based self-supervised Su-Net for carotid plaque ultrasound image segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107957. [PMID: 38061113 DOI: 10.1016/j.cmpb.2023.107957] [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: 12/12/2022] [Revised: 11/17/2023] [Accepted: 11/27/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND OBJECTIVES Total Plaque Area (TPA) measurement is critical for early diagnosis and intervention of carotid atherosclerosis in individuals with high risk for stroke. The delineation of the carotid plaques is necessary for TPA measurement, and deep learning methods can automatically segment the plaque and measure TPA from carotid ultrasound images. A large number of labeled images is essential for training a good deep learning model, but it is very difficult to collect such large labeled datasets for carotid image segmentation in clinical practice. Self-supervised learning can provide a possible solution to improve the deep-learning models on small labeled training datasets by designing a pretext task to pre-train the models without using the segmentation masks. However, the existing self-supervised learning methods do not consider the feature presentations of object contours. METHODS In this paper, we propose an image registration-based self-supervised learning method and a stacked U-Net (SSL-SU-Net) for carotid plaque ultrasound image segmentation, which can better exploit the semantic features of carotid plaque contours in self-supervised task training. RESULTS Our network was trained on different numbers of labeled images (n = 10, 33, 50 and 100 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The network trained on the entire SPARC dataset was then directly applied to an independent dataset collected in Zhongnan hospital (n = 497, Wuhan, China). For the 44 subjects tested on the SPARC dataset, our method yielded a DSC of 80.25-89.18% and the produced TPA measurements, which were strongly correlated with manual segmentation (r = 0.965-0.995, ρ< 0.0001). For the Zhongnan dataset, the DSC was 90.3% and algorithm TPAs were strongly correlated with manual TPAs (r = 0.985, ρ< 0.0001). CONCLUSIONS The results demonstrate that our proposed method yielded excellent performance and good generalization ability when trained on a small labeled dataset, facilitating the use of deep learning in carotid ultrasound image analysis and clinical practice. The code of our algorithm is available https://github.com/a610lab/Registration-SSL.
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Affiliation(s)
- Jing Ding
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, China
| | - Ran Zhou
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, China.
| | - Xiaoyue Fang
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, China
| | - Furong Wang
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Ji Wang
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, China
| | - Haitao Gan
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, China.
| | - Aaron Fenster
- Imaging Research Laboratories, Robarts Research Institute, Western University, London N6A 5K8, Ontario, Canada
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Jafrasteh B, Lubián-López SP, Benavente-Fernández I. A deep sift convolutional neural networks for total brain volume estimation from 3D ultrasound images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107805. [PMID: 37738840 DOI: 10.1016/j.cmpb.2023.107805] [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: 12/21/2022] [Revised: 08/04/2023] [Accepted: 09/07/2023] [Indexed: 09/24/2023]
Abstract
Preterm infants are a highly vulnerable population. The total brain volume (TBV) of these infants can be accurately estimated by brain ultrasound (US) imaging which enables a longitudinal study of early brain growth during Neonatal Intensive Care (NICU) admission. Automatic estimation of TBV from 3D images increases the diagnosis speed and evades the necessity for an expert to manually segment 3D images, which is a sophisticated and time consuming task. We develop a deep-learning approach to estimate TBV from 3D ultrasound images. It benefits from deep convolutional neural networks (CNN) with dilated residual connections and an additional layer, inspired by the fuzzy c-Means (FCM), to further separate the features into different regions, i.e. sift layer. Therefore, we call this method deep-sift convolutional neural networks (DSCNN). The proposed method is validated against three state-of-the-art methods including AlexNet-3D, ResNet-3D, and VGG-3D, for TBV estimation using two datasets acquired from two different ultrasound devices. The results highlight a strong correlation between the predictions and the observed TBV values. The regression activation maps are used to interpret DSCNN, allowing TBV estimation by exploring those pixels that are more consistent and plausible from an anatomical standpoint. Therefore, it can be used for direct estimation of TBV from 3D images without needing further image segmentation.
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Affiliation(s)
- Bahram Jafrasteh
- Biomedical Research and Innovation Institute of Cádiz (INiBICA), Puerta del Mar University, Cádiz, Spain.
| | - Simón Pedro Lubián-López
- Biomedical Research and Innovation Institute of Cádiz (INiBICA), Puerta del Mar University, Cádiz, Spain; Division of Neonatology, Department of Paediatrics, Puerta del Mar University Hospital, Cádiz, Spain.
| | - Isabel Benavente-Fernández
- Biomedical Research and Innovation Institute of Cádiz (INiBICA), Puerta del Mar University, Cádiz, Spain; Division of Neonatology, Department of Paediatrics, Puerta del Mar University Hospital, Cádiz, Spain; Area of Paediatrics, Department of Child and Mother Health and Radiology, Medical School, University of Cádiz, Cádiz, Spain.
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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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Ottakath N, Al-Maadeed S, Zughaier SM, Elharrouss O, Mohammed HH, Chowdhury MEH, Bouridane A. Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions. Diagnostics (Basel) 2023; 13:2614. [PMID: 37568976 PMCID: PMC10417708 DOI: 10.3390/diagnostics13152614] [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: 06/15/2023] [Revised: 07/25/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima-media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.
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Affiliation(s)
- Najmath Ottakath
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar; (S.A.-M.); (O.E.); (H.H.M.)
| | - Somaya Al-Maadeed
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar; (S.A.-M.); (O.E.); (H.H.M.)
| | | | - Omar Elharrouss
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar; (S.A.-M.); (O.E.); (H.H.M.)
| | - Hanadi Hassen Mohammed
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar; (S.A.-M.); (O.E.); (H.H.M.)
| | | | - Ahmed Bouridane
- Centre for Data Analytics and Cybersecurity, University of Sharjah, Sharjah 27272, United Arab Emirates;
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Spence JD. Assessment of atherosclerosis: should coronary calcium score and intima-media thickness be replaced by ultrasound measurement of carotid plaque burden and vessel wall volume? Curr Opin Lipidol 2023; 34:126-132. [PMID: 37093105 DOI: 10.1097/mol.0000000000000880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
PURPOSE OF REVIEW To describe the uses of vessel wall volume (VWV) and measurement of carotid plaque burden, as total plaque area (TPA) and total plaque volume (TPV), and to contrast them with measurement of carotid intima-media thickness (IMT) and coronary calcium (CAC). RECENT FINDINGS Measurement of carotid plaque burden (CPB) is useful for risk stratification, research into the genetics and biology of atherosclerosis, for measuring effects of new therapies for atherosclerosis, and for treatment of high-risk patients with severe atherosclerosis. It is as predictive of risk as CAC, with important advantages. IMT is only a weak predictor of risk and changes so little over time that it is not useful for assessing effects of therapy. SUMMARY Measurement of CPB and VWV are far superior to measurement of carotid IMT in many ways, and should replace it. Vessel wall volume can be measured in persons with no plaque as an alternative to IMT. There are important advantages of CPB over coronary calcium; CPB should be more widely used in vascular prevention.
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Affiliation(s)
- J David Spence
- Professor Emeritus of Neurology, Western University, and Director, Stroke Prevention & Atherosclerosis Research Centre, Robarts Research Institute, 1400 Western Road, London, ON N6G 2V4, Canada
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Li L, Hu Z, Huang Y, Zhu W, Zhao C, Wang Y, Chen M, Yu J. BP-Net: Boundary and perfusion feature guided dual-modality ultrasound video analysis network for fibrous cap integrity assessment. Comput Med Imaging Graph 2023; 107:102246. [PMID: 37210966 DOI: 10.1016/j.compmedimag.2023.102246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 05/23/2023]
Abstract
Ultrasonography is one of the main imaging methods for monitoring and diagnosing atherosclerosis due to its non-invasiveness and low-cost. Automatic differentiation of carotid plaque fibrous cap integrity by using multi-modal ultrasound videos has significant diagnostic and prognostic value for cardiovascular and cerebrovascular disease patients. However, the task faces several challenges, including high variation in plaque location and shape, the absence of analysis mechanism focusing on fibrous cap, the lack of effective mechanism to capture the relevance among multi-modal data for feature fusion and selection, etc. To overcome these challenges, we propose a new target boundary and perfusion feature guided video analysis network (BP-Net) based on conventional B-mode ultrasound and contrast-enhanced ultrasound videos for assessing the integrity of fibrous cap. Based on our previously proposed plaque auto-tracking network, in our BP-Net, we further introduce the plaque edge attention module and reverse mechanism to focus the dual video analysis on the fiber cap of plaques. Moreover, to fully explore the rich information on the fibrous cap and inside/outside of the plaque, we propose a feature fusion module for B-mode and contrast video to filter out the most valuable features for fibrous cap integrity assessment. Finally, multi-head convolution attention is proposed and embedded into transformer-based network, which captures semantic features and global context information to obtain accurate evaluation of fibrous caps integrity. The experimental results demonstrate that the proposed method has high accuracy and generalizability with an accuracy of 92.35% and an AUC of 0.935, which outperforms than the state-of-the-art deep learning based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application.
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Affiliation(s)
- Leyin Li
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Zhaoyu Hu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yunqian Huang
- Department of Ultrasound, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wenqian Zhu
- Department of Ultrasound, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Chengqian Zhao
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Man Chen
- Department of Ultrasound, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China.
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12
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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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13
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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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14
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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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15
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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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16
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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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17
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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.
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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.
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18
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Li F, Li W, Gao X, Liu R, Xiao B. DCNet: Diversity convolutional network for ventricle segmentation on short-axis cardiac magnetic resonance images. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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19
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Yuan Y, Li C, Zhang K, Hua Y, Zhang J. HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images. Diagnostics (Basel) 2022; 12:diagnostics12112852. [PMID: 36428911 PMCID: PMC9689104 DOI: 10.3390/diagnostics12112852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/09/2022] [Accepted: 11/13/2022] [Indexed: 11/19/2022] Open
Abstract
Carotid artery stenotic plaque segmentation in ultrasound images is a crucial means for the analysis of plaque components and vulnerability. However, segmentation of severe stenotic plaques remains a challenging task because of the heterogeneities of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated HRU-Net transfer learning method for segmenting carotid plaques, using the limited images. The HRU-Net is based on the U-Net encoder−decoder paradigm, and cross-domain knowledge is transferred for plaque segmentation by fine-tuning the pretrained ResNet-50. Moreover, a cropped-blood-vessel image augmentation is customized for the plaque position constraint during training only. Moreover, hybrid atrous convolutions (HACs) are designed to derive diverse long-range dependences for refined plaque segmentation that are used on high-level semantic layers to exploit the implicit discrimination features. The experiments are performed on 115 images; Firstly, the 10-fold cross-validation, using 40 images with severe stenosis plaques, shows that the proposed method outperforms some of the state-of-the-art CNN-based methods on Dice, IoU, Acc, and modified Hausdorff distance (MHD) metrics; the improvements on metrics of Dice and MHD are statistically significant (p < 0.05). Furthermore, our HRU-Net transfer learning method shows fine generalization performance on 75 new images with varying degrees of plaque stenosis, and it may be used as an alternative for automatic noisy plaque segmentation in carotid ultrasound images clinically.
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Affiliation(s)
- Yanchao Yuan
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- National Engineering Research Center of Telemedicine and Telehealth, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Hefei Innovation Research Institute, Beihang University, Hefei 230012, China
| | - Cancheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Hefei Innovation Research Institute, Beihang University, Hefei 230012, China
| | - Ke Zhang
- Department of Vascular Ultrasonography, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yang Hua
- Department of Vascular Ultrasonography, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Beijing Diagnostic Center of Vascular Ultrasound, Beijing 100053, China
- Center of Vascular Ultrasonography, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing 100069, China
- Correspondence: (Y.H.); (J.Z.)
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Hefei Innovation Research Institute, Beihang University, Hefei 230012, China
- Correspondence: (Y.H.); (J.Z.)
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20
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Zhang L, Li X, Lyu Q, Shi G. Imaging diagnosis and research progress of carotid plaque vulnerability. JOURNAL OF CLINICAL ULTRASOUND : JCU 2022; 50:905-912. [PMID: 35801515 DOI: 10.1002/jcu.23266] [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: 07/25/2021] [Revised: 05/26/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
Ischemic stroke (IS) exhibits a high disability rate, mortality, and recurrence rate, imposing a serious threat to human survival and health. Its occurrence is affected by various factors. Although the previous research has demonstrated that the occurrence of IS is mainly associated with lumen stenosis caused by carotid atherosclerotic plaque (AP), recent studies have revealed that many patients will still suffer from IS even with mild carotid artery lumen stenosis. Blood supply disturbance causes 10% of IS to the corresponding cerebral blood supply area caused by carotid vulnerable plaque. Thrombus blockage of distal branch vessels caused by rupture of vulnerable carotid plaque is the main cause of ischemic stroke. Therefore, how to accurately evaluate vulnerable plaque and intervene as soon as possible is a problem that needs to be solved in clinic. The vulnerability of plaque is determined by its internal components, including thin and incomplete fibrous cap, necrotic lipid core, intra-plaque hemorrhage, intra-plaque neovascularization, and ulcerative plaque formation. The development of imaging technology enables the routine detection of AP vulnerability. By analyzing the pathological changes, characteristics, and formation mechanism of carotid plaque vulnerability, this article aims to explore the modern imaging methods which can be used to identify plaque composition and plaque vulnerability to provide a reference basis for disease diagnosis and differential diagnosis.
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Affiliation(s)
- Lianlian Zhang
- Yancheng Clinical College of Xuzhou Medical University, The First peolie's Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Xia Li
- Affiliated Hospital of Jiangsu medical vocational college, The Third People's Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Qi Lyu
- Taizhou People's Hospital, Taizhou, China
| | - Guofu Shi
- Affiliated Hospital of Jiangsu medical vocational college, The Third People's Hospital of Yancheng, Yancheng, Jiangsu, China
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21
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Saba L, Antignani PL, Gupta A, Cau R, Paraskevas KI, Poredos P, Wasserman B, Kamel H, Avgerinos ED, Salgado R, Caobelli F, Aluigi L, Savastano L, Brown M, Hatsukami T, Hussein E, Suri JS, Mansilha A, Wintermark M, Staub D, Montequin JF, Rodriguez RTT, Balu N, Pitha J, Kooi ME, Lal BK, Spence JD, Lanzino G, Marcus HS, Mancini M, Chaturvedi S, Blinc A. International Union of Angiology (IUA) consensus paper on imaging strategies in atherosclerotic carotid artery imaging: From basic strategies to advanced approaches. Atherosclerosis 2022; 354:23-40. [DOI: 10.1016/j.atherosclerosis.2022.06.1014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 12/24/2022]
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22
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Sharifzadeh M, Benali H, Rivaz H. Investigating Shift Variance of Convolutional Neural Networks in Ultrasound Image Segmentation. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:1703-1713. [PMID: 35344491 DOI: 10.1109/tuffc.2022.3162800] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
While accuracy is an evident criterion for ultrasound image segmentation, output consistency across different tests is equally crucial for tracking changes in regions of interest in applications such as monitoring the patients' response to treatment, measuring the progression or regression of the disease, reaching a diagnosis, or treatment planning. Convolutional neural networks (CNNs) have attracted rapidly growing interest in automatic ultrasound image segmentation recently. However, CNNs are not shift-equivariant, meaning that, if the input translates, e.g., in the lateral direction by one pixel, the output segmentation may drastically change. To the best of our knowledge, this problem has not been studied in ultrasound image segmentation or even more broadly in ultrasound images. Herein, we investigate and quantify the shift-variance problem of CNNs in this application and further evaluate the performance of a recently published technique, called BlurPooling, for addressing the problem. In addition, we propose the Pyramidal BlurPooling method that outperforms BlurPooling in both output consistency and segmentation accuracy. Finally, we demonstrate that data augmentation is not a replacement for the proposed method. Source code is available at http://code.sonography.ai.
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23
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Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2014349. [PMID: 35509862 PMCID: PMC9061007 DOI: 10.1155/2022/2014349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 03/31/2022] [Indexed: 11/18/2022]
Abstract
Atherosclerotic carotid plaques have been shown to be closely associated with the risk of stroke. Since patients with symptomatic carotid plaques have a greater risk for stroke, stroke risk stratification based on the classification of carotid plaques into symptomatic or asymptomatic types is crucial in diagnosis, treatment planning, and medical treatment monitoring. A deep learning technique would be a good choice for implementing classification. Usually, to acquire a high-accuracy classification, a specific network architecture needs to be designed for a given classification task. In this study, we propose an object-specific four-path network (OSFP-Net) for stroke risk assessment by integrating ultrasound carotid plaques in both transverse and longitudinal sections of the bilateral carotid arteries. Each path of the OSFP-Net comprises of a feature extraction subnetwork (FE) and a feature downsampling subnetwork (FD). The FEs in the four paths use the same network structure to automatically extract features from ultrasound images of carotid plaques. The FDs use different object-specific pooling strategies for feature downsampling based on the observation that the sizes and shapes in the feature maps obtained from FEs should be different. The object-specific pooling strategies enable the network to accept arbitrarily sized carotid plaques as input and to capture a more informative context for improving the classification accuracy. Extensive experimental studies on a clinical dataset consisting of 333 subjects with 1332 carotid plaques show the superiority of our OSFP-Net against several state-of-the-art deep learning-based methods. The experimental results demonstrate better clinical agreement between the ground truth and the prediction, which indicates its great potential for use as a risk stratification and as a monitoring tool in the management of patients at risk for stroke.
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24
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FRDD-Net: Automated Carotid Plaque Ultrasound Images Segmentation Using Feature Remapping and Dense Decoding. SENSORS 2022; 22:s22030887. [PMID: 35161631 PMCID: PMC8838852 DOI: 10.3390/s22030887] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/18/2022] [Accepted: 01/19/2022] [Indexed: 11/17/2022]
Abstract
Automated segmentation and evaluation of carotid plaques ultrasound images is of great significance for the diagnosis and early intervention of high-risk groups of cardiovascular and cerebrovascular diseases. However, it remains challenging to develop such solutions due to the relatively low quality of ultrasound images and heterogenous characteristics of carotid plaques. To address those problems, in this paper, we propose a novel deep convolutional neural network, FRDD-Net, with an encoder–decoder architecture to automatically segment carotid plaques. We propose the feature remapping modules (FRMs) and incorporate them into the encoding and decoding blocks to ameliorate the reliability of acquired features. We also propose a new dense decoding mechanism as part of the decoder, thus promoting the utilization efficiency of encoded features. Additionally, we construct a compound loss function to train our network to further enhance its robustness in the face of numerous cases. We train and test our network in multiple carotid plaque ultrasound datasets and our method yields the best performance compared to other state-of-the-art methods. Further ablation studies consistently show the advancement of our proposed architecture.
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25
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Liu R, Liu M, Sheng B, Li H, Li P, Song H, Zhang P, Jiang L, Shen D. NHBS-Net: A Feature Fusion Attention Network for Ultrasound Neonatal Hip Bone Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3446-3458. [PMID: 34106849 DOI: 10.1109/tmi.2021.3087857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Ultrasound is a widely used technology for diagnosing developmental dysplasia of the hip (DDH) because it does not use radiation. Due to its low cost and convenience, 2-D ultrasound is still the most common examination in DDH diagnosis. In clinical usage, the complexity of both ultrasound image standardization and measurement leads to a high error rate for sonographers. The automatic segmentation results of key structures in the hip joint can be used to develop a standard plane detection method that helps sonographers decrease the error rate. However, current automatic segmentation methods still face challenges in robustness and accuracy. Thus, we propose a neonatal hip bone segmentation network (NHBS-Net) for the first time for the segmentation of seven key structures. We design three improvements, an enhanced dual attention module, a two-class feature fusion module, and a coordinate convolution output head, to help segment different structures. Compared with current state-of-the-art networks, NHBS-Net gains outstanding performance accuracy and generalizability, as shown in the experiments. Additionally, image standardization is a common need in ultrasonography. The ability of segmentation-based standard plane detection is tested on a 50-image standard dataset. The experiments show that our method can help healthcare workers decrease their error rate from 6%-10% to 2%. In addition, the segmentation performance in another ultrasound dataset (fetal heart) demonstrates the ability of our network.
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26
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Chen Y, Liu J, Luo X, Luo J. ApodNet: Learning for High Frame Rate Synthetic Transmit Aperture Ultrasound Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3190-3204. [PMID: 34048340 DOI: 10.1109/tmi.2021.3084821] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Two-way dynamic focusing in synthetic transmit aperture (STA) beamforming can benefit high-quality ultrasound imaging with higher lateral spatial resolution and contrast resolution. However, STA requires the complete dataset for beamforming in a relatively low frame rate and transmit power. This paper proposes a deep-learning architecture to achieve high frame rate STA imaging with two-way dynamic focusing. The network consists of an encoder and a joint decoder. The encoder trains a set of binary weights as the apodizations of the high-frame-rate plane wave transmissions. In this respect, we term our network ApodNet. The decoder can recover the complete dataset from the acquired channel data to achieve dynamic transmit focusing. We evaluate the proposed method by simulations at different levels of noise and in-vivo experiments on the human biceps brachii and common carotid artery. The experimental results demonstrate that ApodNet provides a promising strategy for high frame rate STA imaging, obtaining comparable lateral resolution and contrast resolution with four-times higher frame rate than conventional STA imaging in the in-vivo experiments. Particularly, ApodNet improves contrast resolution of the hypoechoic targets with much shorter computational time when compared with other high-frame-rate methods in both simulations and in-vivo experiments.
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27
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Li L, Hu Z, Huang Y, Zhu W, Wang Y, Chen M, Yu J. Automatic multi-plaque tracking and segmentation in ultrasonic videos. Med Image Anal 2021; 74:102201. [PMID: 34562695 DOI: 10.1016/j.media.2021.102201] [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: 02/08/2021] [Revised: 06/28/2021] [Accepted: 07/26/2021] [Indexed: 01/14/2023]
Abstract
Carotid plaque tracking and segmentation in ultrasound videos is the premise for subsequent plaque property evaluation and treatment plan development. However, the task is quite challenging, as it needs to address the problems of poor image quality, plaque shape variations among frames, the existence of multiple plaques, etc. To overcome these challenges, we propose a new automatic multi-plaque tracking and segmentation (AMPTS) framework. AMPTS consists of three modules. The first module is a multi-object detector, in which a Dual Attention U-Net is proposed to detect multiple plaques and vessels simultaneously. The second module is a set of single-object trackers that can utilize the previous tracking results efficiently and achieve stable tracking of the current target by using channel attention and a ranking strategy. To make the first module and the second module work together, a parallel tracking module based on a simplified 'tracking-by-detection' mechanism is proposed to solve the challenge of tracking object variation. Extensive experiments are conducted to compare the proposed method with several state-of-the-art deep learning based methods. The experimental results demonstrate that the proposed method has high accuracy and generalizability with a Dice similarity coefficient of 0.83 which is 0.16, 0.06 and 0.27 greater than MAST (Lai et al., 2020), Track R-CNN (Voigtlaender et al., 2019) and VSD (Yang et al., 2019) respectively and has made significant improvements on seven other indicators. In the additional Testing set 2, our method achieved a Dice similarity coefficient of 0.80, an accuracy of 0.79, a precision of 0.91, a Recall 0.70, a F1 score of 0.79, an AP@0.5 of 0.92, an AP@0.7 of 0.74, and an expected average overlap of 0.79. Numerous ablation studies suggest the effectiveness of each proposed component and the great potential for multiple carotid plaques tracking and segmentation in clinical practice.
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Affiliation(s)
- Leyin Li
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Zhaoyu Hu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yunqian Huang
- Department of Ultrasound, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wenqian Zhu
- Department of Ultrasound, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Man Chen
- Department of Ultrasound, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China.
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Zhou R, Guo F, Azarpazhooh MR, Hashemi S, Cheng X, Spence JD, Ding M, Fenster A. Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images. IEEE J Biomed Health Inform 2021; 25:2967-2977. [PMID: 33600328 DOI: 10.1109/jbhi.2021.3060163] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentations and TPA measurements; however, it requires large datasets and manual annotations for training with unknown performance on new datasets. A UNet++ ensemble algorithm was proposed to segment plaques from 2D carotid ultrasound images, trained on three small datasets (n = 33, 33, 34 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The ensemble was also trained on the entire SPARC dataset and tested with a different dataset (n = 497, Zhongnan Hospital, China). Algorithm and manual segmentations were compared using Dice-similarity-coefficient (DSC), and TPAs were compared using the difference ( ∆TPA), Pearson correlation coefficient (r) and Bland-Altman analyses. Segmentation variability was determined using the intra-class correlation coefficient (ICC) and coefficient-of-variation (CoV). For 44 SPARC subjects, algorithm DSC was 83.3-85.7%, and algorithm TPAs were strongly correlated (r = 0.985-0.988; p < 0.001) with manual results with marginal biases (0.73-6.75) mm 2 using the three training datasets. Algorithm ICC for TPAs (ICC = 0.996) was similar to intra- and inter-observer manual results (ICC = 0.977, 0.995). Algorithm CoV = 6.98% for plaque areas was smaller than the inter-observer manual CoV (7.54%). For the Zhongnan dataset, DSC was 88.6% algorithm and manual TPAs were strongly correlated (r = 0.972, p < 0.001) with ∆TPA = -0.44 ±4.05 mm 2 and ICC = 0.985. The proposed algorithm trained on small datasets and segmented a different dataset without retraining with accuracy and precision that may be useful clinically and for research.
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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.
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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
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