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Abstract #3494

Automatic Segmentation of Carotid Vessel Wall Using Convolutional Neural Network

Li Chen1, Jie Sun2, Wei Zhang3, Thomas S Hatsukami4, Jianrong Xu3, Jenq-Neng Hwang1, and Chun Yuan2

1Electrical Engineering, University of Washington, Seattle, WA, United States, 2Radiology, University of Washington, Seattle, WA, United States, 3Radiology, Renji Hospital, Shanghai, China, 4Surgery, University of Washington, Seattle, WA, United States

Accurate vessel wall segmentation on black-blood MRI is an important but difficult task. Using previously annotated carotid vessel wall contours by human reviewers, a convolutional neural network (CNN) was trained to predict vessel wall region from the combination of T1-weighted and time-of-flight images. Compared with human segmentation results, the CNN-based model achieved a Dice similarity coefficient of 0.86±0.06 and a correlation coefficient of 0.96 (0.94, 0.97) in measuring vessel wall area. Fast and accurate vessel wall segmentation may help fully realize the potential of vessel wall MRI in monitoring atherosclerosis progression or regression in serial studies and clinical trials.

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