Sammendrag
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 ground-truth
of the dataset using watershedding, and several strategies for data augmentation that allowed to train robustly the U-Net to perform the segmentation. Finally, we deployed the trained network freely available.
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