Vessel wall (VW) MRI has been used to characterize atherosclerotic plaques but the review process is complex. To facilitate the translation of VWMRI into clinical application, we utilized deep convolutional neural networks (CNN) to distinguish between normal and atherosclerotic carotid arteries automatically in black-blood (BB) VWMRI. Trained with a dataset that contains both normal and diseased carotid arteries with expert labeling, an integrative deep CNN model was developed and yielded better automatic diagnosis accuracy of carotid atherosclerosis (85.18%) compared with other existing methods. This model may be used as an initial screening to separate normal from diseased arteries.
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