• DocumentCode
    1375902
  • Title

    A digital brain atlas for surgical planning, model-driven segmentation, and teaching

  • Author

    Kikinis, Ron ; Shenton, Martha E. ; Iosifescu, D.V. ; McCarley, Robert W. ; Saiviroonporn, Pairash ; Hokama, Hiroto H. ; Robatino, Andre ; Metcalf, David ; Wible, Cynthia G. ; Portas, Chiara M. ; Donnino, Robert M. ; Jolesz, Ferenc A.

  • Author_Institution
    Dept. of Radiol., Harvard Med. Sch., Boston, MA, USA
  • Volume
    2
  • Issue
    3
  • fYear
    1996
  • fDate
    9/1/1996 12:00:00 AM
  • Firstpage
    232
  • Lastpage
    241
  • Abstract
    We developed a three-dimensional (3D) digitized atlas of the human brain to visualize spatially complex structures. It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a combination of automated and supervised segmentation methods to define regions of interest based on neuroanatomical knowledge. We also used 3D surface rendering techniques to create a brain atlas that would allow us to visualize complex 3D brain structures. We further linked this Information to script files in order to preserve both spatial information and neuroanatomical knowledge. We present here the application of the atlas for visualization in surgical planning far model-driven segmentation and for the teaching of neuroanatomy. This digitized human brain has the potential to provide important reference information for the planning of surgical procedures. It can also serve as a powerful teaching tool, since spatial relationships among neuroanatomical structures can be more readily envisioned when the user is able to view and rotate the structures in 3D space. Moreover, each element of the brain atlas is associated with a name tag, displayed by a user controlled pointer. The atlas holds a major promise as a template for model-driven segmentation. Using this technique, many regions of interest can be characterized simultaneously on new brain images
  • Keywords
    biomedical education; brain models; computer aided instruction; data visualisation; medical computing; rendering (computer graphics); biomedical visualization; brain atlas; digital brain atlas; human brain; magnetic resonance imaging; model-driven segmentation; neuroanatomical knowledge; supervised segmentation methods; surgical planning; teaching; Biomedical imaging; Brain modeling; Data visualization; Education; Humans; Image segmentation; Laboratories; Magnetic resonance imaging; Neuroscience; Surgery;
  • fLanguage
    English
  • Journal_Title
    Visualization and Computer Graphics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1077-2626
  • Type

    jour

  • DOI
    10.1109/2945.537306
  • Filename
    537306