Abstract #0983

Automatic AHA Classification of Carotid Atherosclerotic Lesions in Multicontrast MR Images using Deep Learning

Jifan Li¹, Shuo Chen¹, Xihai Zhao¹, Yuan Chun², and Rui Li¹

¹Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China, Beijing, China, ²Vascular Imaging Laboratory, Department of Radiology, University of Washington, Seattle, WA, United States, Seattle, WA, United States

In this study, we aimed to develop a convolutional neural network (CNN) to classify carotid atherosclerotic lesions in high-resolution multicontrast MR images automatically using the modified American Heart Association (AHA) classification scheme as criteria. The network was trained on a large number of plaque images combined with lesion type labeled by experienced radiologists. Transfer learning was utilized to take the advantage of state-of-the-art CNN pre-trained on ImageNet dataset. The accuracy of lesion type classification achieved 85.1% with preprocessing and fine-tuning of the network.

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