MRI is gaining popularity for identifying atherosclerosis, a common disease caused by the accumulation of cholesterol in arteries. To identify vulnerable plaque, the components in plaque need to be segmented by radiologist manually, which is both hard and tedious. Previous attempts to solve the problem using probability maps are limited by their accuracy. We leverage the recently developed convolutional neural networks (CNN) to build a model based on 1,000 subjects automatically, achieving significantly better accuracy in almost every metric over traditional methods.
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