Education, Science, Technology, Innovation and Life
Open Access
Sign In

Research of Carotid Plaque Segmentation and Classification in MRI Images Based on Artificial Intelligence

Download as PDF

DOI: 10.23977/jipta.2023.060106 | Downloads: 26 | Views: 560


Jianqin Chen 1, Hui Xiao 2, Mingjun Lin 2, Yong Hong 2, Chaomin Chen 2, Xin Zhang 2


1 Dongguan Changan Hospital, Dongguan, Guangdong, China
2 School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Corresponding Author

Xin Zhang


Atherosclerosis (AS) is one of the important factors leading to acute cardiovascular and cerebrovascular diseases. Carotid plaque (CP) is formed in the process of carotid atherosclerosis. Accurate identification and classification of carotid plaque by medical imaging is of great significance for the prevention and treatment of acute cardiovascular diseases. Currently commonly used methods of carotid plaque examination include ultrasound (US), digital subtraction angiography (DSA), computed tomography angiography (CTA), and magnetic resonance imaging (MRI). With the rapid development of computer graphics processing hardware, the application of artificial intelligence (AI) in medical image processing is becoming more and more mature. This paper reviews recent studies on the detection, segmentation and classification of carotid plaques in MRI based on machine learning and deep learning models, and summarizes and looks into the current challenges and research trends in this field.


Atherosclerosis, carotid artery plaque, machine learning, deep learning


Jianqin Chen, Hui Xiao, Mingjun Lin, Yong Hong, Chaomin Chen, Xin Zhang, Research of Carotid Plaque Segmentation and Classification in MRI Images Based on Artificial Intelligence. Journal of Image Processing Theory and Applications (2023) Vol. 6: 62-66. DOI:


[1] De Korte C. L., Fekkes S., Nederveen A. J., Manniesing R., & Hansen H. R. H. (2016). Mechanical characterization of carotid arteries and atherosclerotic plaques. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 63(10), 1613-1623.
[2] TWCotRoCHaDi C. (2021). China cardiovascular health and disease report 2020. Chin J Circ, 36, 521-45.
[3] Wu Jixin, Cheng Wei, Hu Biqiong, et al. (2019). Study on the relationship between carotid plaque and stenosis and ischemic stroke. Collection.
[4] Li Dandan, Mei Jun, Zhou Qingbing, et al. (2022). Progress in the pathogenesis of atherosclerosis mediated by innate immunit. Chinese Journal of Atherosclerosis, 30 (1): 71-76.
[5] Huang Liuming (Summary), Wang Jisheng (Reviser). (2012). Advances in imaging of carotid plaques. Medical Review, v.18 (19): 3264-3266.
[6] Saba L., Sanagala S. S., Gupta S. K., Koppula V. K., Johri A. M., Khanna N. N., & Suri J. S. (2021). Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: A narrative review for stroke application. Annals of Translational Medicine, 9(14).
[7] Wu Qiuwen, Li Yuxin, Huang Lei, et al. (2019). Research progress of machine learning algorithm in imaging classification of carotid plaques. Chinese Clinical Neuroscience, 27 (4): 8.
[8] Kong Linjun, Wang Xiwen, Bao Yunchao, et al. (2021). A review of medical image segmentation based on depth learning. Radio Communication Technology, 47 (2): 10.
[9] Hassan M., Murtza I., Hira A., Ali S., & Kifayat K. (2019). Robust spatial fuzzy GMM based MRI segmentation and carotid artery plaque detection in ultrasound images. Computer methods and programs in biomedicine, 175, 179-192.
[10] Jamthikar A. D., Gupta D., Mantella L. E., Saba L., Laird J. R., Johri A. M., & Suri J. S. (2021). Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: A 500 participants study. The International Journal of Cardiovascular Imaging, 37, 1171-1187.
[11] Wu D., Cui G., Huang X., Chen Y., Liu G., Ren L., & Li Y. (2022). An accurate and explainable ensemble learning method for carotid plaque prediction in an asymptomatic population. Computer Methods and Programs in Biomedicine, 221, 106842.
[12] Banchhor S. K., Londhe N. D., Araki T., Saba L., Radeva P., Khanna N. N., & Suri J. S. (2018). Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: a review. Computers in Biology and Medicine, 101, 184-198.
[13] Li Jifan, Chen Shuo, Zhang Qiang, et al. (2019). Research on Segmentation method of Multimodal MR carotid artery Imaging based on U-Net Neural Network. Chinese Journal of Radiology, 53 (12): 5.
[14] Lange R. T., & Lippa S. M. (2017). Sensitivity and specificity should never be interpreted in isolation without consideration of other clinical utility metrics. The Clinical Neuropsychologist, 31(6-7), 1015-1028.
[15] Xu W., Yang X., Li Y., Jiang G., Jia S., Gong Z, & Zhang N. (2022). Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images. Frontiers in Neuroscience, 16.
[16] Zhang Q., Qiao H., Dou J., Sui B., Zhao X., Chen Z, & Chen H. (2019). Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning. Magnetic Resonance Imaging, 60, 93-100.
[17] Dong Y., Pan Y., Zhao X., Li R., Yuan C., & Xu W. (2017, May). Identifying carotid plaque composition in MRI with convolutional neural networks. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 1-8). IEEE.
[18] Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D, & Rabinovich A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
[19] Simonyan K., & Zisserman A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
[20] He K., Zhang X., Ren S., & Sun J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[21] Van Engelen A., Van Dijk A. C., Truijman M. T., Van't Klooster R., Van Opbroek A., van der Lugt A, & de Bruijne M. (2014). Multi-center MRI carotid plaque component segmentation using feature normalization and transfer learning. IEEE transactions on medical imaging, 34(6), 1294-1305.

Downloads: 1076
Visits: 95849

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.