  Removing skull artifacts from functional magnetic images (fMRI) is a well
understood and frequently encountered problem. Because the fMRI field has grown
mostly due to human studies, many new tools were developed to handle human
data. Nonetheless, these tools are not equally useful to handle the data
derived from animal studies, especially from rodents. This represents a major
problem to the field because rodent studies generate larger datasets from
larger populations, which implies that preprocessing these images manually to
remove the skull becomes a bottleneck in the data analysis pipeline. In this
study, we address this problem by implementing a neural network based method
that uses a U-Net architecture to segment the brain area into a mask and
removing the skull and other tissues from the image. We demonstrate several
strategies to speed up the process of generating the training dataset using
watershedding and several strategies for data augmentation that allowed to
train faster the U-Net to perform the segmentation. Finally, we deployed the
trained network freely available.
