Meeting Banner
Abstract #1051

Automatic Segmentation of Carotid Vessel Wall in Multi-Contrast Blackblood Images using Deep Learning

Jifan Li1, Shuo Chen1, Xihai Zhao1, Chun Yuan1,2, and Rui Li1

1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2Vascular Imaging Laboratory, Department of Radiology, University of Washington, Seattle, WA, United States

In this work, we proposed an automatic approach for segmentation of carotid vessel wall in multi-contrast blackblood images, using a fine-tuning U-net neural network. The U-net network consists of an encoder path that captures context and reduces data dimension and a symmetric decoder path that enables precise localization and high resolution. The fine-tuning was utilized to accommodate multi-contrast images input. The pixel-level sensitivity, specificity and IoU of our model achieved 0.869, 0.987 and 0.751 on the test data set, respectively.

This abstract and the presentation materials are available to members only; a login is required.

Join Here