Many neuroimaging studies are applied to monkeys as pathologies and environmental exposures can be studied in
well-controlled settings and environment. In this work, we present a framework for the use of an atlas based, fully
automatic segmentation of brain tissues, lobar parcellations, subcortical structures and the regional extraction
of Diffusion Tensor Imaging (DTI) properties. We first built a structural atlas from training images by iterative,
joint deformable registration into an unbiased average image. On this atlas, probabilistic tissue maps, a lobar
parcellation and subcortical structures were determined. This information is applied to each subjects structural
image via affine, followed by deformable registration. The affinely transformed atlas is employed for a joint T1
and T2 based tissue classification. The deformed parcellation regions mask the tissue segmentations to define the
parcellation for white and gray matter separately. Each subjects structural image is then non-rigidly matched
with its DTI image by normalized mutual information, b-spline based registration. The DTI property histograms
were then computed using the probabilistic white matter information for each lobar parcellation.
We successfully built an average atlas using a developmental training datasets of 18 cases aged 16-34 months.
Our framework was successfully applied to over 50 additional subjects in the age range of 9 70 months. The
probabilistically weighted FA average in the corpus callosum region showed the largest increase over time in the
observed age range. Most cortical regions show modest FA increase, whereas the cerebellums FA values remained
The individual methods used in this segmentation framework have been applied before, but their combination is novel, as is their application to macaque MRI data. Furthermore, this is the first study to date looking at the DTI properties of the developing macaque brain.