Cover Image

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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Karstensen, Lennart (Author) , Ritter, Jacqueline (Author) , Hatzl, Johannes (Author) , Pätz, Torben (Author) , Langejürgen, Jens (Author) , Uhl, Christian (Author) , Mathis-Ullrich, Franziska (Author)
Format: Article (Journal)
Language:English
Published: November 2022
In: International journal of computer assisted radiology and surgery
Year: 2022, Volume: 17, Issue: 11, Pages: 2033-2040
ISSN:1861-6429
DOI:10.1007/s11548-022-02646-8
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s11548-022-02646-8
Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s11548-022-02646-8
Get full text
Author Notes:Lennart Karstensen, Jacqueline Ritter, Johannes Hatzl, Torben Pätz, Jens Langejürgen, Christian Uhl, Franziska Mathis-Ullrich
Description
Summary: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.
Item Description:Artikel online veröffentlicht: 23. Mai 2022
Gesehen am 23.09.2024
Physical Description:Online Resource
ISSN:1861-6429
DOI:10.1007/s11548-022-02646-8

Similar Items