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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...

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Hauptverfasser: Karstensen, Lennart (VerfasserIn) , Ritter, Jacqueline (VerfasserIn) , Hatzl, Johannes (VerfasserIn) , Pätz, Torben (VerfasserIn) , Langejürgen, Jens (VerfasserIn) , Uhl, Christian (VerfasserIn) , Mathis-Ullrich, Franziska (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: November 2022
In: International journal of computer assisted radiology and surgery
Year: 2022, Jahrgang: 17, Heft: 11, Pages: 2033-2040
ISSN:1861-6429
DOI:10.1007/s11548-022-02646-8
Online-Zugang: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
Volltext
Verfasserangaben:Lennart Karstensen, Jacqueline Ritter, Johannes Hatzl, Torben Pätz, Jens Langejürgen, Christian Uhl, Franziska Mathis-Ullrich
Beschreibung
Zusammenfassung: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.
Beschreibung:Artikel online veröffentlicht: 23. Mai 2022
Gesehen am 23.09.2024
Beschreibung:Online Resource
ISSN:1861-6429
DOI:10.1007/s11548-022-02646-8

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