Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm

Ya-Fang Chen; Zhen-Jie Chen; You-Yu Lin; Zhi-Qiang Lin; Chun-Nuan Chen; Mei-Li Yang; Jin-Yin Zhang; Yuan-Zhe Li; Yi Wang; Yin-Hui Huang

doi:10.3389/fcvm.2023.1101765

Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm

Front Cardiovasc Med. 2023 Feb 24:10:1101765. doi: 10.3389/fcvm.2023.1101765. eCollection 2023.

Authors

Affiliations

¹ Department of Neurology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
² Department of Neurology, Anxi County Hospital, Quanzhou, Fujian, China.
³ Department of Neurology, Jinjiang Municipal Hospital, Shanghai Sixth People's Hospital Fujian Campus, Quanzhou, Fujian, China.
⁴ Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.

Abstract

Introduction: The primary factor for cardiovascular disease and upcoming cardiovascular events is atherosclerosis. Recently, carotid plaque texture, as observed on ultrasonography, is varied and difficult to classify with the human eye due to substantial inter-observer variability. High-resolution magnetic resonance (MR) plaque imaging offers naturally superior soft tissue contrasts to computed tomography (CT) and ultrasonography, and combining different contrast weightings may provide more useful information. Radiation freeness and operator independence are two additional benefits of M RI. However, other than preliminary research on MR texture analysis of basilar artery plaque, there is currently no information addressing MR radiomics on the carotid plaque.

Methods: For the automatic segmentation of MRI scans to detect carotid plaque for stroke risk assessment, there is a need for a computer-aided autonomous framework to classify MRI scans automatically. We used to detect carotid plaque from MRI scans for stroke risk assessment pre-trained models, fine-tuned them, and adjusted hyperparameters according to our problem.

Results: Our trained YOLO V3 model achieved 94.81% accuracy, RCNN achieved 92.53% accuracy, and MobileNet achieved 90.23% in identifying carotid plaque from MRI scans for stroke risk assessment. Our approach will prevent incorrect diagnoses brought on by poor image quality and personal experience.

Conclusion: The evaluations in this work have demonstrated that this methodology produces acceptable results for classifying magnetic resonance imaging (MRI) data.

Keywords: MRI carotid plaque; YOLO V3; deep learning; stroke risk; transfer learning.