Automatic parcellation of neonate brains

In the absence of a neonatal template with cortical subregion labels, it can be extremely difficult to obtain cortical parcellation of new neonatal brain images automatically. We address this problem by utilizing adult templates with rich cortical annotation and a neonatal template with simple tissue labels. Theoretical feasibility is assured because of the preservation of brain putative cytoarchitectonic boundaries from birth to adulthood. We use large deformation registration to propagate neuroanatomical labels from adult to neonatal brain and perform multi-atlas labeling based on accurate prior-based tissue segmentation. We evaluate the repeatability of the labeling by cross-validation with training and testing data.