Application of Artificial Intelligence Methods in Carotid Artery Segmentation: A Review

TY - JOUR

T1 - Application of Artificial Intelligence Methods in Carotid Artery Segmentation

T2 - A Review

AU - Wang, Yu

AU - Yao, Yudong

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2023

Y1 - 2023

N2 - The carotid artery is one of the most important blood vessels that supply blood to the brain. If thrombus occurs, it may cause cerebral ischemic stroke and endanger life. Carotid intima-media thickness and stability of carotid plaque are essential indicators for predicting stroke, which can be measured through medical image segmentation. Therefore, automatic and accurate carotid artery image segmentation and measurement of carotid intima-media thickness and the area and volume of carotid plaque are of great significance for stroke risk prediction and treatment. However, due to the complex shape of the carotid artery and the characteristics of carotid artery imaging, the traditional methods (such as threshold methods, region growth methods) can not segment the carotid artery very well. In recent years, researchers have taken artificial intelligence (traditional machine learning and deep learning) as a critical research method for carotid artery segmentation and extensive research has been performed with satisfactory results. In this paper, we present a comprehensive review of carotid artery segmentation using artificial intelligence methods. We first briefly introduce medical image processing methods and artificial intelligence methods. And then, review and summarize the application of artificial intelligence segmentation methods in carotid artery segmentation (including carotid lumen, media-adventitia, lumen-intima, and plaques). Finally, the challenges of current artificial intelligence methods in carotid artery segmentation are analyzed.

AB - The carotid artery is one of the most important blood vessels that supply blood to the brain. If thrombus occurs, it may cause cerebral ischemic stroke and endanger life. Carotid intima-media thickness and stability of carotid plaque are essential indicators for predicting stroke, which can be measured through medical image segmentation. Therefore, automatic and accurate carotid artery image segmentation and measurement of carotid intima-media thickness and the area and volume of carotid plaque are of great significance for stroke risk prediction and treatment. However, due to the complex shape of the carotid artery and the characteristics of carotid artery imaging, the traditional methods (such as threshold methods, region growth methods) can not segment the carotid artery very well. In recent years, researchers have taken artificial intelligence (traditional machine learning and deep learning) as a critical research method for carotid artery segmentation and extensive research has been performed with satisfactory results. In this paper, we present a comprehensive review of carotid artery segmentation using artificial intelligence methods. We first briefly introduce medical image processing methods and artificial intelligence methods. And then, review and summarize the application of artificial intelligence segmentation methods in carotid artery segmentation (including carotid lumen, media-adventitia, lumen-intima, and plaques). Finally, the challenges of current artificial intelligence methods in carotid artery segmentation are analyzed.

KW - Carotid artery segmentation

KW - deep learning

KW - machine learning

KW - medical image processing

KW - plaque segmentation

UR - https://www.scopus.com/pages/publications/85148464234

UR - https://www.scopus.com/pages/publications/85148464234#tab=citedBy

U2 - 10.1109/ACCESS.2023.3243162

DO - 10.1109/ACCESS.2023.3243162

M3 - Review article

SN - 2169-3536

VL - 11

SP - 13846

EP - 13858

JO - IEEE Access

JF - IEEE Access

ER -