Brain tumor segmentation using holistically nested neural networks in MRI images.

Journal: Medical physics
Published Date: Aug 20, 2017

Abstract

PURPOSE: Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images.

Authors

  • Ying Zhuge
    Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Andra V Krauze
    Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Holly Ning
    Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Jason Y Cheng
    Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Barbara C Arora
    Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Kevin Camphausen
    Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Robert W Miller
    Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.

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