Enabling machine learning in X-ray-based procedures via realistic simulation of image formation.

Journal: International journal of computer assisted radiology and surgery
PMID: 31187399

Abstract

PURPOSE: Machine learning-based approaches now outperform competing methods in most disciplines relevant to diagnostic radiology. Image-guided procedures, however, have not yet benefited substantially from the advent of deep learning, in particular because images for procedural guidance are not archived and thus unavailable for learning, and even if they were available, annotations would be a severe challenge due to the vast amounts of data. In silico simulation of X-ray images from 3D CT is an interesting alternative to using true clinical radiographs since labeling is comparably easy and potentially readily available.

Authors

  • Mathias Unberath
    Johns Hopkins University, Baltimore, MD, USA.
  • Jan-Nico Zaech
    Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA.
  • Cong Gao
    State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.
  • Bastian Bier
    Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA.
  • Florian Goldmann
    Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA.
  • Sing Chun Lee
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Javad Fotouhi
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Russell Taylor
    Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
  • Mehran Armand
    Johns Hopkins University, Baltimore, MD, USA.
  • Nassir Navab
    Chair for Computer Aided Medical Procedures & Augmented Reality, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.

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