SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learning

Sovesh Mohapatra, Advait Gosai, Gottfried Schlaug

Published 2023-04-10, updated 2023-04-15Version 2

Brain extraction is a critical preprocessing step in neuroimaging studies, involving the separation of brain tissue from non-brain tissue using MRI data. FSL's Brain Extraction Tool (BET) is the current gold standard but is prone to errors due to image quality issues. The Segment Anything Model (SAM) by Meta AI has shown promising zero-shot segmentation potential. This paper compares SAM with BET for brain extraction on diverse brain scans, considering image quality, MRI sequences, and lesion locations. Results demonstrate that SAM outperforms BET in various evaluation parameters, particularly in cases with signal inhomogeneities, non-isotropic voxel resolutions, or lesions near the brain's outer regions and meninges. SAM's superior performance indicates its potential as a more accurate, robust, and versatile tool for brain extraction and segmentation applications.