• DocumentCode
    2720867
  • Title

    Automatic cortical surface parcellation based on fiber density information

  • Author

    Zhang, Degang ; Guo, Lei ; Li, Gang ; Nie, Jingxin ; Fan Deng ; Li, Kaiming ; Hu, Xintao ; Zhang, Tuo ; Jiang, Xi ; Zhu, Dajiang ; Zhao, Qun ; Liu, Tianming

  • Author_Institution
    Sch. of Autom., Northwestern Polytech. Univ., Xi´´an, China
  • fYear
    2010
  • fDate
    14-17 April 2010
  • Firstpage
    1133
  • Lastpage
    1136
  • Abstract
    It is widely believed that the structural connectivity of a brain region largely determines its function. High resolution Diffusion Tensor Imaging (DTI) is now able to image the axonal fibers in vivo and the DTI tractography result provides rich connectivity information. In this paper, a novel method is proposed to employ fiber density information for automatic cortical parcellation based on the premise that fibers connecting to the same cortical region should be within the same functional brain network and their aggregation on the cortex can define a functionally coherent region. This method consists of three steps. Firstly, the fiber density is calculated on the cortical surface. Secondly, a flow field is obtained by calculating the fiber density gradient and a flow field tracking method is utilized for cortical parcellation. Finally, an atlas-based warping method is used to label the parcellated regions. Our method was applied to parcellate and label the cortical surfaces of eight healthy brain DTI images, and interesting results are obtained. In addition, the labeled regions are used as ROIs to construct structural networks for different brains, and the graph properties of these networks are measured.
  • Keywords
    biomedical MRI; brain; neurophysiology; DTI tractography; atlas-based warping method; automatic cortical parcellation; automatic cortical surface parcellation; axonal fibers; brain region; connectivity information; fiber density information; functional brain network; graph properties; high resolution diffusion tensor imaging; structural connectivity; Automation; Computer science; Diffusion tensor imaging; Flowcharts; Image reconstruction; Image resolution; In vivo; Joining processes; Physics; Surface reconstruction; Cortical surface parcellation; fiber density; structure network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on
  • Conference_Location
    Rotterdam
  • ISSN
    1945-7928
  • Print_ISBN
    978-1-4244-4125-9
  • Electronic_ISBN
    1945-7928
  • Type

    conf

  • DOI
    10.1109/ISBI.2010.5490193
  • Filename
    5490193