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Abstract #2842

Automated brain extraction of multi-sequence MRI using artificial neural networks

Irada Tursunova1, Marianne Schell1, Fabian Isensee2, Ulf Neuberger1, Gianluca Brugnara1, David Bonekamp3, Wolfgang Wick4,5, Martin Bendszus1, Klaus H Maier-Hein2, and Philipp Kickingereder1

1Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany, 2Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, 3Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 4Neurology, Heidelberg University Hospital, Heidelberg, Germany, 5Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), Heidelberg, Germany

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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