  Brain extraction in magnetic resonance imaging (MRI) data is an important
segmentation step in many neuroimaging preprocessing pipelines. Image
segmentation is one of the research fields in which deep learning had the
biggest impact in recent years enabling high precision segmentation with
minimal compute. Consequently, traditional brain extraction methods are now
being replaced by deep learning-based methods. Here, we used a unique dataset
comprising 568 T1-weighted (T1w) MR images from 191 different studies in
combination with cutting edge deep learning methods to build a fast,
high-precision brain extraction tool called deepbet. deepbet uses LinkNet, a
modern UNet architecture, in a two stage prediction process. This increases its
segmentation performance, setting a novel state-of-the-art performance during
cross-validation with a median Dice score (DSC) of 99.0% on unseen datasets,
outperforming current state of the art models (DSC = 97.8% and DSC = 97.9%).
While current methods are more sensitive to outliers, resulting in Dice scores
as low as 76.5%, deepbet manages to achieve a Dice score of > 96.9% for all
samples. Finally, our model accelerates brain extraction by a factor of ~10
compared to current methods, enabling the processing of one image in ~2 seconds
on low level hardware.
