Spatio-temporal deep learning models for tip force estimation during needle insertion.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 30, 2019

Abstract

PURPOSE: Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. Hence, a number of approaches to estimate the forces at the needle have been proposed. Yet, integrating sensors into the needle tip is challenging and a careful calibration is required to obtain good force estimates.

Authors

  • Nils Gessert
    Hamburg University of Technology, Schwarzenbergstraße 95 21073, Hamburg. Electronic address: mfbeg@sfu.ca.
  • Torben Priegnitz
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
  • Thore Saathoff
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
  • Sven-Thomas Antoni
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
  • David Meyer
    Department of Urology, University Hospital Schleswig-Holstein, Kiel, Germany.
  • Moritz Franz Hamann
    Department of Urology, University Hospital Schleswig-Holstein, Kiel, Germany.
  • Klaus-Peter Jünemann
    Department of Urology, University Hospital Schleswig-Holstein, Kiel, Germany.
  • Christoph Otte
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
  • Alexander Schlaefer
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany. schlaefer@tuhh.de.

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