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Cerebral Cortex January 2004; 14:11-22
© Oxford University Press 2004

Automatically Parcellating the Human Cerebral Cortex

Bruce Fischl1,2, André van der Kouwe1, Christophe Destrieux3,4, Eric Halgren1, Florent Ségonne2, David H. Salat1, Evelina Busa1, Larry J. Seidman5,6,7, Jill Goldstein5, David Kennedy2,8, Verne Caviness7, Nikos Makris8, Bruce Rosen1 and Anders M. Dale1,2

1 MGH/MIT/Harvard Medical School Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, MGH, 13th Street, Charlestown, MA 02129, USA, 2 Artificial Intelligence Laboratory, MIT, 13th Street, Charlestown, MA 02129, USA, 3 Laboratoire d’Anatomie, Faculté de Médecine/CHRU, Tours, France, 4 INSERM U316, Tours, France, 5 Harvard Department of Psychiatry at Massachusetts Mental Health Center, 13th Street, Charlestown, MA 02129, USA, 6 Harvard Department of Psychiatry at Massachusetts General Hospital, 13th Street, Charlestown, MA 02129, USA, 7 Harvard Institute of Psychiatric Epidemiology and Genetics, 13th Street, Charlestown, MA 02129, USA, 8 Center for Morphometric Analysis, Department of Neurology, MGH, 13th Street, Charlestown, MA 02129, USA

We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.


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