Yin Guo1, Li Chen2, Dongxiang Xu3, Rui Li4, Xihai Zhao4, Thomas S. Hatsukami5, and Chun Yuan1,3
1Bioengineering, University of Washington, Seattle, WA, United States, 2Electrical Engineering, University of Washington, Seattle, WA, United States, 3Radiology, University of Washington, Seattle, WA, United States, 4Biomedical Engineering, Tsinghua University, Beijing, China, 5Surgery, University of Washington, Seattle, WA, United States
1Bioengineering, University of Washington, Seattle, WA, United States, 2Electrical Engineering, University of Washington, Seattle, WA, United States, 3Radiology, University of Washington, Seattle, WA, United States, 4Biomedical Engineering, Tsinghua University, Beijing, China, 5Surgery, University of Washington, Seattle, WA, United States
In this work, we developed AI-CASCADE, a fully automated solution for quantitative tissue characterization of carotid MRI, including artery localization, vessel wall segmentation, artery registration and plaque component segmentation.
Fig 1. Workflow of AI-CASCADE
Fig 3. Visualization of composition segmentation. Blue-Ca, yellow-LRNC, orange-IPH.