  Brain extraction is a critical preprocessing step in various neuroimaging
studies, particularly enabling accurate separation of brain from non-brain
tissue and segmentation of relevant within-brain tissue compartments and
structures using Magnetic Resonance Imaging (MRI) data. FSL's Brain Extraction
Tool (BET), although considered the current gold standard for automatic brain
extraction, presents limitations and can lead to errors such as over-extraction
in brains with lesions affecting the outer parts of the brain, inaccurate
differentiation between brain tissue and surrounding meninges, and
susceptibility to image quality issues. Recent advances in computer vision
research have led to the development of the Segment Anything Model (SAM) by
Meta AI, which has demonstrated remarkable potential in zero-shot segmentation
of objects in real-world scenarios. In the current paper, we present a
comparative analysis of brain extraction techniques comparing SAM with a widely
used and current gold standard technique called BET on a variety of brain scans
with varying image qualities, MR sequences, and brain lesions affecting
different brain regions. We find that SAM outperforms BET based on average Dice
coefficient, IoU and accuracy metrics, particularly in cases where image
quality is compromised by signal inhomogeneities, non-isotropic voxel
resolutions, or the presence of brain lesions that are located near (or
involve) the outer regions of the brain and the meninges. In addition, SAM has
also unsurpassed segmentation properties allowing a fine grain separation of
different issue compartments and different brain structures. These results
suggest that SAM has the potential to emerge as a more accurate, robust and
versatile tool for a broad range of brain extraction and segmentation
applications.
