Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver
Karstensen L, Ritter J, Hatzl J, Paetz T, Langejuergen J, Uhl C, Mathis-Ullrich F (2022)
Publication Type: Journal article
Publication year: 2022
Journal
Book Volume: 17
Pages Range: 2033-2040
Journal Issue: 11
DOI: 10.1007/s11548-022-02646-8
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. Methods: We trained and evaluated a neural network-based controller via deep reinforcement learning in a finite element simulation to navigate the venous system of a porcine liver without human-generated data. The behavior is compared to manual expert navigation, and real-world transferability is evaluated. Results: The controller achieves a success rate of 100% in simulation. The controller applies a wiggling behavior, where the guidewire tip is continuously rotated alternately clockwise and counterclockwise like the human expert applies. In the ex vivo porcine liver, the success rate drops to 30%, because either the wrong branch is probed, or the guidewire becomes entangled. Conclusion: In this work, we prove that a learning-based controller is capable of learning human-like guidewire navigation behavior without human-generated data, therefore, mitigating the requirement to produce resource-intensive human-generated training data. Limitations are the restriction to one vessel geometry, the neglected safeness of navigation, and the reduced transferability to the real world.
Authors with CRIS profile
Involved external institutions
Universitätsklinikum Heidelberg
Germany (DE)
Fraunhofer-Institut für Produktionstechnik und Automatisierung (IPA)
Germany (DE)
Fraunhofer-Institut für Bildgestützte Medizin (MEVIS)
Germany (DE)
Karlsruhe Institute of Technology (KIT)
Germany (DE)
Germany (DE)
Fraunhofer-Institut für Produktionstechnik und Automatisierung (IPA)
Germany (DE)
Fraunhofer-Institut für Bildgestützte Medizin (MEVIS)
Germany (DE)
Karlsruhe Institute of Technology (KIT)
Germany (DE)
How to cite
APA:
Karstensen, L., Ritter, J., Hatzl, J., Paetz, T., Langejuergen, J., Uhl, C., & Mathis-Ullrich, F. (2022). Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver. International Journal of Computer Assisted Radiology and Surgery, 17(11), 2033-2040. https://dx.doi.org/10.1007/s11548-022-02646-8
MLA:
Karstensen, Lennart, et al. "Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver." International Journal of Computer Assisted Radiology and Surgery 17.11 (2022): 2033-2040.
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