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Abstract #3503

Robustness of a fully automated brain segmentation tool for multiple MRI protocols: test for clinical applications

Zifei LIANG 1,2 , Xiaohai HE 1 , Andreia V. Faria 2 , Kenishi Oishi 2 , Yue Li 3 , Kinya Okada 2,4 , Can Ceritoglu 5 , Xiaoying Tang 5 , Michael Miller 5 , and Susumu Mori 2,6

1 College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China, 2 Johns Hopkins University School of Medicine, BALTIMORE, MD, United States, 3 AnatomyWorks,LLC, BALTIMORE, MD, United States, 4 MitsubishiTanabe Pharma Corporation, Kawagishi, Japan, 5 Center for Imaging Science, Johns Hopkins University, BALTIMORE, MD, United States, 6 Kennedy Krieger Institute, BALTIMORE, MD, United States

We tested the robustness of a multi-atlas whole-brain segmentation tool against different imaging protocols. We measured the volumes of 286 structures in 72 healthy brains from ADNI database, from three scanner manufacturers and two field strengths. The protocol impact, that explained 1.5% of the data variation, is far smaller than age effect, that explained 10.4% of the data variation, indicating that the data pooled from multiple sources can be used to evaluate biological effects. This type of robust technology is a key to apply quantitative analysis for clinical diagnosis, in which highly consistent image protocol cannot be expected.

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