Automatically parcellating the human cerebral cortex.
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.
Fischl
Bruce
B
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, MGH/MIT/Harvard Medical School, Massachusetts General Hospital, 13th Street, Charlestown, MA 02129, USA. fischl@nmr.mgh.harvard.edu
van der Kouwe
André
A
Destrieux
Christophe
C
Halgren
Eric
E
Ségonne
Florent
F
Salat
David H
DH
Busa
Evelina
E
Seidman
Larry J
LJ
Goldstein
Jill
J
Kennedy
David
D
Caviness
Verne
V
Makris
Nikos
N
Rosen
Bruce
B
Dale
Anders M
AM
D000465:Algorithms; D016880:Anisotropy; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology*; D007839:Functional Laterality; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D008959:Models, Neurological; D015233:Models, Statistical; D012559:Schizophrenia / Q000473:pathology