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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.

Pubmed ID: 14654453

Authors

  • Fischl B
  • van der Kouwe A
  • Destrieux C
  • Halgren E
  • S├ęgonne F
  • Salat DH
  • Busa E
  • Seidman LJ
  • Goldstein J
  • Kennedy D
  • Caviness V
  • Makris N
  • Rosen B
  • Dale AM

Journal

Cerebral cortex (New York, N.Y. : 1991)

Publication Data

January 5, 2004

Associated Grants

  • Agency: NIMH NIH HHS, Id: MH 56956
  • Agency: NCRR NIH HHS, Id: P41 RR 14075
  • Agency: NINDS NIH HHS, Id: R01 NS 34189
  • Agency: NINDS NIH HHS, Id: R01 NS 39581
  • Agency: NCRR NIH HHS, Id: R01 RR 13609
  • Agency: NCRR NIH HHS, Id: R01 RR 16594-01A1

Mesh Terms

  • Algorithms
  • Anisotropy
  • Artificial Intelligence
  • Bayes Theorem
  • Brain Mapping
  • Cerebral Cortex
  • Functional Laterality
  • Humans
  • Image Processing, Computer-Assisted
  • Markov Chains
  • Models, Neurological
  • Models, Statistical
  • Schizophrenia