Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver.

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

Abstract

PURPOSE: The navigation of endovascular guidewires is a dexterous task where physicians and patients can benefit from automation. Machine learning-based controllers are promising to help master this task. However, human-generated training data are scarce and resource-intensive to generate. We investigate if a neural network-based controller trained without human-generated data can learn human-like behaviors.

Authors

  • Lennart Karstensen
    Fraunhofer IPA, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. Lennart.karstensen@ipa.fraunhofer.de.
  • Jacqueline Ritter
    Fraunhofer IPA, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
  • Johannes Hatzl
    Department of Vascular and Endovascular Surgery, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
  • Torben Pätz
    Fraunhofer MEVIS, Max-von-Laue-Str. 2, 28359, Bremen, Germany.
  • Jens Langejürgen
    Fraunhofer IPA, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
  • Christian Uhl
    Department of Vascular and Endovascular Surgery, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
  • Franziska Mathis-Ullrich
    Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany.