Force estimation from 4D OCT data in a human tumor xenograft mouse model

© 2020 Maximilian Neidhardt et al., published by De Gruyter, Berlin/Boston 2020. Minimally invasive robotic surgery offer benefits such as reduced physical trauma, faster recovery and lesser pain for the patient. For these procedures, visual and haptic feedback to the surgeon is crucial when operating surgical tools without line-of-sight with a robot. External force sensors are biased by friction at the tool shaft and thereby cannot estimate forces between tool tip and tissue. As an alternative, vision-based force estimation was proposed. Here, interaction forces are directly learned from deformation observed by an external imaging system. Recently, an approach based on optical coherence tomography and deep learning has shown promising results. However, most experiments are performed on ex-vivo tissue. In this work, we demonstrate that models trained on dead tissue do not perform well in in vivo data. We performed multiple experiments on a human tumor xenograft mouse model, both on in vivo, perfused tissue and dead tissue. We compared two deep learning models in different training scenarios. Training on perfused, in vivo data improved model performance by 24% for in vivo force estimation.

Subjects

4D deep learning

force estimation

optical coherence tomography

xenograft mouse model

DDC Class

570: Biowissenschaften, Biologie

610: Medizin

Funding(s)

Mechatronisch geführte Mikronavigation von Nadeln in Weichgewebe

More Funding Information

Deutsche Forschungsgemeinschaft (DFG)

TUHH i3 initiative

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

[23645504 - Force estimation.pdf

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683.81 KB

Format

Adobe PDF

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Force estimation from 4D OCT data in a human tumor xenograft mouse model