Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex

  • Sarah Parisot
  • Salim Arslan
  • Jonathan Passerat-Palmbach
  • William M. WellsIII
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)

Abstract

The analysis of the connectome of the human brain provides key insight into the brain’s organisation and function, and its evolution in disease or ageing. Parcellation of the cortical surface into distinct regions in terms of structural connectivity is an essential step that can enable such analysis. The estimation of a stable connectome across a population of healthy subjects requires the estimation of a groupwise parcellation that can capture the variability of the connectome across the population. This problem has solely been addressed in the literature via averaging of connectivity profiles or finding correspondences between individual parcellations a posteriori. In this paper, we propose a groupwise parcellation method of the cortex based on diffusion MR images (dMRI). We borrow ideas from the area of cosegmentation in computer vision and directly estimate a consistent parcellation across different subjects and scales through a spectral clustering approach. The parcellation is driven by the tractography connectivity profiles, and information between subjects and across scales. Promising qualitative and quantitative results on a sizeable data-set demonstrate the strong potential of the method.

Keywords

Spectral Cluster Geodesic Distance Cortical Surface Structural Connectivity Kullback Leibler 
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.

Notes

Acknowledgements

The research leading to these results has received funding from NIH grant P41EB015902 and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement no. 319456. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sarah Parisot
    • 1
  • Salim Arslan
    • 1
  • Jonathan Passerat-Palmbach
    • 1
  • William M. WellsIII
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis Group, Department of ComputingImperial College LondonLondonUK
  2. 2.Surgical Planning LaboratoryBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

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