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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 RIS Download

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

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Associated grants

  • Agency: NCRR NIH HHS, Id: R01 RR 13609
  • Agency: NCRR NIH HHS, Id: R01 RR 16594-01A1
  • Agency: NINDS NIH HHS, Id: R01 NS 34189
  • Agency: NCRR NIH HHS, Id: P41 RR 14075
  • Agency: NCRR NIH HHS, Id: P41 RR014075
  • Agency: NINDS NIH HHS, Id: R01 NS 39581
  • Agency: NIMH NIH HHS, Id: MH 56956
  • Agency: NIMH NIH HHS, Id: R01 MH056956
  • Agency: NCRR NIH HHS, Id: R01 RR016594
  • Agency: NEI NIH HHS, Id: R01 EY013609
  • Agency: NINDS NIH HHS, Id: R01 NS034189

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