The surface models provided an excellent basis for the analysis of the structural and functional properties of the cerebral cortex, but not for subcortical and ventricular structures. At the time, segmentation tools were limited to a small number of tissue classes (e.g. gray matter, white matter and CSF) or subcortical structures (e.g. Louis Collins’ excellent work (Collins and Evans, 1997)). However, no tools existed that would provide a labeling of each voxel in the brain into semantically meaningful classes such as hippocampus, amygdala, thalamus, etc.…. Part of the difficulty in generating such a segmentation was the variability in the histological composition of these structures. For example, while pallidum and caudate are both gray matter nuclei, the significantly higher myelin content of the pallidum make its T1-weighted MR intensity considerably higher than that of the caudate. Even more difficult was the modeling of structures such as the thalamus that showed variability within its boundaries, with darker appearing tissue at the midline and a gradient of brighter intensities as one moves more laterally. In order to resolve these issues we took a
