  The removal of non-brain signal from magnetic resonance imaging (MRI) data,
known as skull-stripping, is an integral component of many neuroimage analysis
streams. Despite their abundance, popular classical skull-stripping methods are
usually tailored to images with specific acquisition properties, namely
near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are
prevalent in research settings. As a result, existing tools tend to adapt
poorly to other image types, such as stacks of thick slices acquired with fast
spin-echo (FSE) MRI that are common in the clinic. While learning-based
approaches for brain extraction have gained traction in recent years, these
methods face a similar burden, as they are only effective for image types seen
during the training procedure. To achieve robust skull-stripping across a
landscape of imaging protocols, we introduce SynthStrip, a rapid,
learning-based brain-extraction tool. By leveraging anatomical segmentations to
generate an entirely synthetic training dataset with anatomies, intensity
distributions, and artifacts that far exceed the realistic range of medical
images, SynthStrip learns to successfully generalize to a variety of real
acquired brain images, removing the need for training data with target
contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image
acquisitions and resolutions across subject populations, ranging from newborn
to adult. We show substantial improvements in accuracy over popular
skull-stripping baselines -- all with a single trained model. Our method and
labeled evaluation data are available at https://w3id.org/synthstrip.
