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 Wick^4,5, 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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