Brain extraction is a preliminary but critical step in many neuroimaging studies and determines the accuracy of subsequent analyses. Standard brain extraction algorithms are, however, limited to the processing of precontrast T1-weighted (T1-w) MRI and frequently fail in the presence of pathologically altered brain. Here we developed a new algorithm based on artificial neuroal networks (ANN) that enables rapid, automated and robust brain extraction irrespective of pathology, sequence type, hardware or acquisition parameters and lays the groundwork for automated, high-throughput processing of neuroimaging data.
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