• DocumentCode
    3242629
  • Title

    A fast and fully automated approach to segment optic nerves on MRI and its application to radiosurgery

  • Author

    Dolz, J. ; Leroy, H.A. ; Reyns, N. ; Massoptier, L. ; Vermandel, M.

  • Author_Institution
    AQUILAB, Loos-les-Lille, France
  • fYear
    2015
  • fDate
    16-19 April 2015
  • Firstpage
    1102
  • Lastpage
    1105
  • Abstract
    Delineating critical structures of the brain is required for advanced radiotherapy technologies to determine whether the dose from the proposed treatment will impair the functionality of those structures. Employing an automatic segmentation computer module in the radiation oncology treatment planning process has the potential to significantly increase the efficiency, cost-effectiveness, and, ultimately, clinical outcome of patients undergoing radiation therapy. Atlas-based segmentation has shown to be a suitable tool for the segmentation of large structures such as the brainstem or the cerebellum. However, smaller structures such as the optic nerves are more difficult to segment. In this work, we present a novel approach to automatically segment the optic nerves, which is based on Support Vector Machines (SVM). Compared to state of the art methods, the presented method obtained a better performance in regards to accuracy, robustness and processing time, being a suitable trade-off between these three factors.
  • Keywords
    biomedical MRI; brain; cancer; eye; image segmentation; medical image processing; neurophysiology; planning; radiation therapy; support vector machines; MRI; SVM; advanced radiotherapy technology; atlas-based segmentation; automatic segmentation computer module; brain structure functionality impairment; brainstem segmentation; cerebellum segmentation; clinical outcome; critical brain structure delineation; fast optic nerve segmentation; fully automated optic nerve segmentation; optic nerve segmentation accuracy; optic nerve segmentation robustness; processing time; proposed treatment dose; radiation oncology treatment planning; radiosurgery application; radiotherapy cost effectiveness; radiotherapy efficiency; support vector machine; Biomedical optical imaging; Brain; Image segmentation; Optical imaging; Support vector machines; Three-dimensional displays; MRI; machine learning; optic nerves segmentation; radiosurgery; support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on
  • Conference_Location
    New York, NY
  • Type

    conf

  • DOI
    10.1109/ISBI.2015.7164064
  • Filename
    7164064