  Brain extraction is a critical preprocessing step in the analysis of MRI
neuroimaging studies and influences the accuracy of downstream analyses. The
majority of brain extraction algorithms are, however, optimized for processing
healthy brains and thus frequently fail in the presence of pathologically
altered brain or when applied to heterogeneous MRI datasets. Here we introduce
a new, rigorously validated algorithm (termed HD-BET) relying on artificial
neural networks that aims to overcome these limitations. We demonstrate that
HD-BET outperforms six popular, publicly available brain extraction algorithms
in several large-scale neuroimaging datasets, including one from a prospective
multicentric trial in neuro-oncology, yielding state-of-the-art performance
with median improvements of +1.16 to +2.11 points for the DICE coefficient and
-0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm
shows robust performance in the presence of pathology or treatment-induced
tissue alterations, is applicable to a broad range of MRI sequence types and is
not influenced by variations in MRI hardware and acquisition parameters
encountered in both research and clinical practice. For broader accessibility
our HD-BET prediction algorithm is made freely available
(http://www.neuroAI-HD.org) and may become an essential component for robust,
automated, high-throughput processing of MRI neuroimaging data.
