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Abstract #4812

Carotid Artery Localization and Lesion Classification on 3D-MERGE MRI using Neural Network and Object Tracking methods

Li Chen1, Huilin Zhao1,2, Niranjan Balu1, Xihai Zhao3, Rui Li3, Jianrong Xu2, Thomas S Hatsukami1, Jenq-Neng Hwang1, and Chun Yuan1

1University of Washington, Seattle, WA, United States, 2Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China, 3Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China

Carotid vessel wall imaging (VWI) with MRI provides additional prognostic value for cerebro/cardiovascular ischemic events, beyond current clinical diagnostic imaging methods. While fast 3D carotid MRI is possible, manual review of the large 3D dataset is time consuming. Automatic identification of artery locations and lesion categories are therefore required for VWI screening protocols. With neural network and object tracking methods, we developed a fully automated analysis tool to find common/internal/external carotid arteries and flag possible high-risk lesion locations. The tool achieved 0.782 Intersection over Union (IoU) for artery localization, and 0.895 sensitivity for high-risk lesion classification.

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