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Abstract #3068

Automated Localization of the Extracranial Carotid Artery in Black Blood Contrast MR Images Using a Deep Learning Approach

SeyyedKazem HashemizadehKolowri1, Nadin Zanaty2,3, Gador Canton2, Niranjan Balu2, Thomas S. Hatsukami2, and Chun Yuan1,2
1Radiology and Imaging Sciences, University of Utah, SALT LAKE CITY, UT, United States, 2Department of Radiology, University of Washington, Seattle, WA, United States, 3Radiology, Zagazig University, Zagazig, Egypt

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Cardiovascular, Vessel Wall ImagingIn this work, a deep learning approach for automated localization of carotid arteries in black blood contrast MR data is proposed. This is the first step in automated analysis of vessel wall imaging data. Carotid arteries supply oxygenated blood to the brain and are susceptible to atherosclerosis, so their vessel wall imaging is of significant importance in clinical evaluations. However, currently only qualitative assessment of VW imaging data relying on visual inspection is implemented in clinics that are not scaleable. Therefore, developing automated image processing tools to quantitatively analyze vessel wall imaging data can have a major clinical impact.

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Keywords