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 neuro...

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Bibliographic Details
Published in:Cerebral cortex (New York, N.Y. 1991) N.Y. 1991), 2004-01, Vol.14 (1), p.11-22
Main Authors: Fischl, Bruce, van der Kouwe, André, Destrieux, Christophe, Halgren, Eric, Ségonne, Florent, Salat, David H., Busa, Evelina, Seidman, Larry J., Goldstein, Jill, Kennedy, David, Caviness, Verne, Makris, Nikos, Rosen, Bruce, Dale, Anders M.
Format: Article
Language:English
Subjects:
Algorithms
Anisotropy
Artificial Intelligence
Bayes Theorem
Brain Mapping - methods
Cerebral Cortex - anatomy & histology
Cerebral Cortex - physiology
Functional Laterality
Humans
Image Processing, Computer-Assisted - methods
Markov Chains
Models, Neurological
Models, Statistical
Schizophrenia - pathology
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Summary: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.
ISSN:1047-3211
1460-2199
1460-2199
DOI:10.1093/cercor/bhg087