Machine learning-based augmented reality for improved surgical scene understanding.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date: Jun 19, 2014

Abstract

In orthopedic and trauma surgery, AR technology can support surgeons in the challenging task of understanding the spatial relationships between the anatomy, the implants and their tools. In this context, we propose a novel augmented visualization of the surgical scene that mixes intelligently the different sources of information provided by a mobile C-arm combined with a Kinect RGB-Depth sensor. Therefore, we introduce a learning-based paradigm that aims at (1) identifying the relevant objects or anatomy in both Kinect and X-ray data, and (2) creating an object-specific pixel-wise alpha map that permits relevance-based fusion of the video and the X-ray images within one single view. In 12 simulated surgeries, we show very promising results aiming at providing for surgeons a better surgical scene understanding as well as an improved depth perception.

Authors

  • Olivier Pauly
    Computer Aided Medical Procedures, Technische Universität, München, Germany; Institute of Biomathematics and Biometry, Helmholtz Zentrum, München, Germany. Electronic address: olivier.pauly@tum.de.
  • Benoit Diotte
    Computer Aided Medical Procedures, Technische Universität, München, Germany.
  • Pascal Fallavollita
    Computer Aided Medical Procedures, Technische Universität, München, Germany.
  • Simon Weidert
    Chirurgische Klinik und Poliklinik Innenstadt, München, Germany.
  • Ekkehard Euler
    Chirurgische Klinik und Poliklinik Innenstadt, München, Germany.
  • 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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