Deep learning in medical imaging and radiation therapy.

Journal: Medical physics
Published Date: Nov 20, 2018

Abstract

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

Authors

  • Berkman Sahiner
    Food and Drug Administration/CDRH, Silver Spring, USA.
  • Aria Pezeshk
  • Lubomir M Hadjiiski
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Xiaosong Wang
    Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD, 20892-1182, USA.
  • Karen Drukker
    Department of Radiology, University of Chicago, Chicago, IL, 60637, USA.
  • Kenny H Cha
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.
  • Maryellen L Giger
    Department of Radiology, University of Chicago, 5841 S Maryland Ave., Chicago, IL, 60637, USA.

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