The corpus callosum (CC) is a structure of interest in many neuroimaging studies of neuro-developmental pathology
such as autism. It plays an integral role in relaying sensory, motor and cognitive information from homologous
regions in both hemispheres.
We have developed a framework that allows automatic segmentation of the corpus callosum and its lobar subdivisions.
Our approach employs constrained elastic deformation of flexible Fourier contour model, and is an
extension of Szekely's 2D Fourier descriptor based Active Shape Model. The shape and appearance model, derived
from a large mixed population of 150+ subjects, is described with complex Fourier descriptors in a principal
component shape space. Using MNI space aligned T1w MRI data, the CC segmentation is initialized on the
mid-sagittal plane using the tissue segmentation. A multi-step optimization strategy, with two constrained steps
and a final unconstrained step, is then applied. If needed, interactive segmentation can be performed via contour
repulsion points. Lobar connectivity based parcellation of the corpus callosum can finally be computed via the
use of a probabilistic CC subdivision model.
Our analysis framework has been integrated in an open-source, end-to-end application called CCSeg both with
a command line and Qt-based graphical user interface (available on NITRC). A study has been performed to
quantify the reliability of the semi-automatic segmentation on a small pediatric dataset. Using 5 subjects randomly
segmented 3 times by two experts, the intra-class correlation coefficient showed a superb reliability (0.99).
CCSeg is currently applied to a large longitudinal pediatric study of brain development in autism.