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An Automated Approach to Connectivity-Based Partitioning of Brain Structures

  • P. A. Cook
  • H. Zhang
  • B. B. Avants
  • P. Yushkevich
  • D. C. Alexander
  • J. C. Gee
  • O. Ciccarelli
  • A. J. Thompson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3749)

Abstract

We present an automated approach to the problem of connectivity-based partitioning of brain structures using diffusion imaging. White-matter fibres connect different areas of the brain, allowing them to interact with each other. Diffusion-tensor MRI measures the orientation of white-matter fibres in vivo, allowing us to perform connectivity-based partitioning non-invasively. Our new approach leverages atlas-based segmentation to automate anatomical labeling of the cortex. White-matter connectivities are inferred using a probabilistic tractography algorithm that models crossing pathways explicitly. The method is demonstrated with the partitioning of the corpus callosum of eight healthy subjects.

Keywords

Probability Density Function Fractional Anisotropy Corpus Callosum Seed Point Automate Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • P. A. Cook
    • 1
  • H. Zhang
    • 2
  • B. B. Avants
    • 2
  • P. Yushkevich
    • 2
  • D. C. Alexander
    • 1
  • J. C. Gee
    • 2
  • O. Ciccarelli
    • 3
  • A. J. Thompson
    • 3
  1. 1.Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonUK
  2. 2.Departments of Computer & Information Science and RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Department of Headache, Brain Injury and Neuroinflammation, Institute of NeurologyUniversity College LondonUK

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