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Automatically parcellating the human cerebral cortex.

Bruce Fischl1, André van der Kouwe, Christophe Destrieux

  • 1Athinoula 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

Cerebral Cortex (New York, N.Y. : 1991)
|December 5, 2003
PubMed
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This study introduces an automated method for labeling brain regions on cortical surface models. The technique accurately labels sulci and gyri, matching manual labeling precision.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Anatomy

Background:

  • Accurate neuroanatomical labeling of the human cortex is crucial for understanding brain function and disease.
  • Manual labeling is time-consuming and subject to inter-rater variability.
  • Automated methods are needed to improve efficiency and consistency in cortical surface analysis.

Purpose of the Study:

  • To develop and validate an automated technique for assigning neuroanatomical labels to cortical surface models.
  • To incorporate geometric and conventional neuroanatomical information for comprehensive labeling.
  • To demonstrate the algorithm's flexibility and accuracy compared to manual labeling.

Main Methods:

  • Utilized a manually labeled training set to estimate probabilistic neuroanatomical information.

Related Experiment Videos

  • Integrated geometric features from cortical surface models.
  • Applied neuroanatomical conventions derived from the training data.
  • Tested the algorithm using two distinct training sets with varying conventions.

    • Main Results:

    • Achieved complete labeling of cortical sulci and gyri on surface models.
    • Demonstrated the algorithm's adaptability to different neuroanatomical conventions.
    • The automated labeling accuracy was found to be comparable to manual labeling.

    Conclusions:

    • The developed technique provides an accurate and flexible automated solution for cortical surface labeling.
    • This method has the potential to streamline neuroimaging research and clinical applications.
    • The approach offers a reliable alternative to manual annotation, enhancing consistency in brain mapping studies.