Abstract #4897

Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network model

Ran Li^1,2, Jie Zheng^1,2, Pamela Woodard^1,2, and Jha Abhinav^1,2

¹Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States, ²Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States

Synopsis

Our objective of this study is to accurately segment and classify carotid atherosclerotic plaque components in a completely automated manner, based on multi-weighted MRI. Specifically, we segmented each pixel using a two-module neural network model. Furthermore, we generated segmentation uncertainty maps with a Bayesian method to evaluate the inherent uncertainty of this segmentation task.

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