Carotid plaques may cause strokes, the composition of which is crucial for assessing the risk. While multi-contrast plaque magnetic resonance imaging (MRI) is a powerful technology, it is both tedious and error-prone for a radiologist to review these images, and traditional analytic algorithm relying on manually crafted features perform poorly as well.
We propose a novel approach with deep convolutional neural network (CNN), to be specific, U-net, to segment these plaque tissues. Some modifications on loss functions, convolution patterns and training protocols help our model perform well. On a dataset of 1098 subjects, we show that we achieve significantly better accuracy than previous models.