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Updated: Sep 22, 2025
Technique of Porcine Liver Procurement and Orthotopic Transplantation using an Active Porto-Caval Shunt
Published on: May 7, 2015
Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine
Lennart Karstensen1,2, Jacqueline Ritter3, Johannes Hatzl4
1Fraunhofer IPA, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. Lennart.karstensen@ipa.fraunhofer.de.
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.
