Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3042
Publisher DOI: 10.1515/cdbme-2020-0022
Title: Force estimation from 4D OCT data in a human tumor xenograft mouse model
Language: English
Authors: Neidhardt, Maximilian 
Gessert, Nils 
Gosau, Tobias 
Kemmling, Julia 
Feldhaus, Susanne 
Schumacher, Udo 
Schlaefer, Alexander 
Keywords: 4D deep learning; force estimation; optical coherence tomography; xenograft mouse model
Issue Date: 1-May-2020
Publisher: de Gruyter
Source: Current Directions in Biomedical Engineering 1 (6): 20200022 (2020)
Abstract (english): 
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.
URI: http://hdl.handle.net/11420/7736
DOI: 10.15480/882.3042
ISSN: 2364-5504
Journal: Current directions in biomedical engineering 
Institute: Medizintechnische Systeme E-1 
Document Type: Article
Project: Mechatronisch geführte Mikronavigation von Nadeln in Weichgewebe 
More Funding information: Deutsche Forschungsgemeinschaft (DFG)
TUHH i3 initiative
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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