Automating Carotid Plaque Detection in MRI

For 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. Deep learning technology has recently permeated several areas of medical study and has taken center stage in modern science and technology (13 (link)). Deep learning technology can fully utilize vast amounts of data, automatically learn the features in the data, accurately and rapidly support clinicians in diagnosis, and increase medical efficiency (14 (link)). In this research, we proposed a deep learning framework based on transfer learning to detect carotid plaque from MRI scans for stroke risk assessment. We used YOLO V3, Mobile Net, and RCNN pre-trained models, fine-tuned them and adjusted hyperparameters according to our dataset. All experiments in this paper are conducted on Intel(R) Celeron(R) CPU N3150 @ 1.60 GHz. The operating system is Windows 64-bit, Python 3.6.6, TensorFlow deep Learning framework 1.8.0, and CUDA 10.1. The proposed framework to address the mentioned research problem is shown in Figure 1.

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Chen Y.F., Chen Z.J., Lin Y.Y., Lin Z.Q., Chen C.N., Yang M.L., Zhang J.Y., Li Y.Z., Wang Y, & Huang Y.H. (2023). Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm. Frontiers in Cardiovascular Medicine, 10, 1101765.

Publication 2023

Carotid Diagnosis Learning transfer Mri scans Plaque Python Risk assessment Stroke

Corresponding Organization :

Other organizations : Second Affiliated Hospital of Fujian Medical University, Fujian Medical University, Shanghai Sixth People's Hospital

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Variable analysis

independent variables

Pre-trained deep learning models (YOLO V3, Mobile Net, RCNN)
Fine-tuning and hyperparameter adjustment of the pre-trained models

dependent variables

Ability to detect carotid plaque from MRI scans for stroke risk assessment

control variables

Intel(R) Celeron(R) CPU N3150 @ 1.60 GHz
Windows 64-bit operating system
Python 3.6.6
TensorFlow deep Learning framework 1.8.0
CUDA 10.1

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