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

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

Author(s)

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

Affiliation(s)

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

Corresponding Author

Xin Zhang

ABSTRACT

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.

KEYWORDS

Atherosclerosis, carotid artery plaque, machine learning, deep learning

CITE THIS PAPER

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: http://dx.doi.org/10.23977/jipta.2023.060106.

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