Model-Guided Segmentation of Corpus Callosum in MR Images

Magnetic resonance imaging (MRI) of the brain, followed by automated segmentation of the corpus callosum (CC) in midsagittal sections have important applications in both clinical neurology and neurocognitive research since the size and shape of the CC are shown to be correlated to sex, age, neurodegenerative diseases and various lateralized behavior in man. Moreover, whole head, multispectral 3D MRI recordings enable voxel-based tissue classification and estimation of total brain volumes, in addition to CC morphometric parameters. We propose a new algorithm that uses both multispectral MRI measurements (intensity values) and prior information about shape (CC template) to segment CC in midsagittal slices with very little user interaction. The algorithm has been tested on a sample of 10 subjects scanned with multispectral 3D MRI, collected for a study of dyslexia, with very good agreement between the manually traced (“true”) CC outline and the detected CC outline. We conclude that the proposed method for CC segmentation is promising for clinical use when multispectral MR images are recorded.