Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators.

Authors

  • Niclas Bockelmann
    Institute for Robotics and Cognitive Systems, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany. bockelmann@rob.uni-luebeck.de.
  • Daniel Schetelig
    Söring GmbH, Justus-von-Liebig-Ring 2, 25451, Quickborn, Germany.
  • Denise Kesslau
    Söring GmbH, Justus-von-Liebig-Ring 2, 25451, Quickborn, Germany.
  • Steffen Buschschlüter
    Söring GmbH, Justus-von-Liebig-Ring 2, 25451, Quickborn, Germany.
  • Floris Ernst
    Institute for Robotics and Cognitive Systems, Universität zu Lübeck, Lübeck, Germany.
  • Matteo Mario Bonsanto
    Department of Neurosurgery, University Hospital Schleswig-Holstein, Ratzeburger Allee 160, 23538, Lübeck, Germany.