State of the Art: Machine Learning Applications in Glioma Imaging.

Journal: AJR. American journal of roentgenology
Published Date: Oct 17, 2018

Abstract

OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas.

Authors

  • Eyal Lotan
    1 Department of Radiology, New York University Langone Medical Center, 660 1st Ave, Rm 336, New York, NY 10016.
  • Rajan Jain
    1 Department of Radiology, New York University Langone Medical Center, 660 1st Ave, Rm 336, New York, NY 10016.
  • Narges Razavian
    1 Department of Computer Science, New York University , New York, New York.
  • Girish M Fatterpekar
    1 Department of Radiology, New York University Langone Medical Center, 660 1st Ave, Rm 336, New York, NY 10016.
  • Yvonne W Lui
    Center for Advanced Imaging Innovation and Research (CAI2R), School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA; Bernard and Irene Schwartz Center for Biomedical Imaging, School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA.

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