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Abstract #2145

Carotid plaque composition segmentation in multi-contrast MRI with U-net

Yin Guo1, Jifan Li2, Dingkun Liu2, JIachen Ji2, Shuo Chen2, Xihai Zhao2, Dongxiang Xu3, Chun Yuan3, and Rui Li2

1Biomedical Engineering, Yale University, New Haven, CT, United States, 2Biomedical Engineering, Tsinghua University, Beijing, China, 3Bioengineering, University of Washington, Seattle, WA, United States

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.

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