  Brain extraction and removal of skull artifacts from magnetic resonance
images (MRI) is an important preprocessing step in neuroimaging analysis. There
are many tools developed to handle human fMRI images, which could involve
manual steps for verifying results from brain segmentation that makes it time
consuming and inefficient. In this study, we will use the segment anything
model (SAM), a freely available neural network released by Meta[4], which has
shown promising results in many generic segmentation applications. We will
analyze the efficiency of SAM for neuroimaging brain segmentation by removing
skull artifacts. The results of the experiments showed promising results that
explore using automated segmentation algorithms for neuroimaging without the
need to train on custom medical imaging dataset.
