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

Brain extraction, a critical preprocessing step in neuroimaging studies, enables the automatic segmentation of brain vs non-brain compartments as well as relevant within-brain tissue compartments and structures. While FSL’s Brain Extraction Tool (BET) is recognized as the gold standard, it often struggles with inaccuracies, especially in brains with outer lesions or compromised image quality. We present an alternative based on Meta AI's Segment Anything Model (SAM), renowned for its zero-shot segmentation capabilities. Our comparative analysis across diverse magnetic resonance imaging (MRI) sequences reveals SAM's superiority over BET, particularly in challenging imaging scenarios. Our study not only underscores SAM's potential for general brain extraction, but also its versatility in segmenting specific intra-brain regions of interest.