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Abstract #3122

Semi-Automated Microbleed Identification on Susceptibility Weighted Images

Samuel Barnes1,2, E. Mark Haacke1

1Wayne State University, Detroit, MI, United States; 2Loma Linda University, Loma Linda, CA, United States

A method to detect microbleeds in the brain in a semi-automated fashion is presented. The goal of this technique is to reduce the processing time of quantifying microbleeds. The semi-automated method compares favorably with manual counting achieving approximately 80% sensitivity and 100% specificity while reducing processing time to under an hour.