Automatically parcellating the human cerebral cortex

Cereb Cortex. 2004 Jan;14(1):11-22. doi: 10.1093/cercor/bhg087.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • 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