Abstract #1051

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

Jifan Li¹, Shuo Chen¹, Xihai Zhao¹, Chun Yuan^1,2, 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.

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