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Abstract #3261

Bayesian Convolutional Neural Network Based Nonhuman Primate Brain Extraction in Fully Three-dimensional Context

Gengyan Zhao1, Fang Liu2, Jonathan A. Oler3, Mary E. Meyerand1,4, Ned H. Kalin3, and Rasmus M. Birn1,3

1Department of Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, 2Department of Radiology, University of Wisconsin - Madison, Madison, WI, United States, 3Department of Psychiatry, University of Wisconsin - Madison, Madison, WI, United States, 4Department of Biomedical Engineering, University of Wisconsin - Madison, Madison, WI, United States

Brain extraction of MR images is an essential step in neuroimaging, but current brain extraction methods are often far from satisfactory on nonhuman primates. To overcome this challenge, we propose a fully-automated brain extraction framework combining deep Bayesian convolutional neural network and fully connected three-dimensional conditional random field. It is not only able to perform accurate brain extraction in a fully three-dimensional context, but also capable of generating uncertainty on each prediction. The proposed method outperforms six popular methods on a 100-subject dataset, and a better performance was verified by different metrics and statistical tests (Bonferroni corrected p-values<10-4).

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