  The development of automated tools for brain morphometric analysis in infants
has lagged significantly behind analogous tools for adults. This gap reflects
the greater challenges in this domain due to: 1) a smaller-scaled region of
interest, 2) increased motion corruption, 3) regional changes in geometry due
to heterochronous growth, and 4) regional variations in contrast properties
corresponding to ongoing myelination and other maturation processes.
Nevertheless, there is a great need for automated image-processing tools to
quantify differences between infant groups and other individuals, because
aberrant cortical morphologic measurements (including volume, thickness,
surface area, and curvature) have been associated with neuropsychiatric,
neurologic, and developmental disorders in children. In this paper we present
an automated segmentation and surface extraction pipeline designed to
accommodate clinical MRI studies of infant brains in a population 0-2
year-olds. The algorithm relies on a single channel of T1-weighted MR images to
achieve automated segmentation of cortical and subcortical brain areas,
producing volumes of subcortical structures and surface models of the cerebral
cortex. We evaluated the algorithm both qualitatively and quantitatively using
manually labeled datasets, relevant comparator software solutions cited in the
literature, and expert evaluations. The computational tools and atlases
described in this paper will be distributed to the research community as part
of the FreeSurfer image analysis package.
