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Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7509)

Abstract

Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our “label fusion” method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults.

Keywords

  • Fractional Anisotropy
  • White Matter Tract
  • Diffusion Tensor Magnetic Resonance Imaging
  • Fractional Anisotropy Image
  • Label Fusion

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.

This study was supported by Grant RO1 HD050735 from the National Institutes of Health (NIH) and Grant 496682 from the National Health and Medical Research Council (NHMRC), Australia.

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Jin, Y. et al. (2012). Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases. In: Yap, PT., Liu, T., Shen, D., Westin, CF., Shen, L. (eds) Multimodal Brain Image Analysis. MBIA 2012. Lecture Notes in Computer Science, vol 7509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33530-3_12

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  • DOI: https://doi.org/10.1007/978-3-642-33530-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33529-7

  • Online ISBN: 978-3-642-33530-3

  • eBook Packages: Computer ScienceComputer Science (R0)