3D Magnetic Resonance (MR) and Diffusion Tensor Imaging (DTI) have become important
noninvasive tools for the study of animal models of brain development and neuropathologies.
Fully automated analysis methods adapted to rodent scale for these images will allow highthroughput
studies. A fundamental first step for most quantitative analysis algorithms is skullstripping,
which refers to the segmentation of the image into two tissue categories, brain and
non-brain. In this manuscript, we present a fully automatic skull-stripping algorithm in an atlasbased
manner. We also demonstrate how to either modify an external atlas or to build an atlas
from the population itself to present a self-contained approach. We applied our method to three
datasets of rat brain scans, at different ages (PND5, PND14 and adult), different study groups
(control, ethanol exposed, intrauterine cocaine exposed), as well as different image acquisition
parameters. We validated our method by comparing the automated skull-strip results to manual
delineations performed by our expert, which showed a discrepancy of less than a single voxel
on average. We thus demonstrate that our algorithm can robustly and accurately perform the
skull-stripping within one voxel of the manual delineation, and in a fraction of the time it takes
a human expert.