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Force estimation from OCT volumes using 3D CNNs

  • Nils Gessert
  • Jens Beringhoff
  • Christoph Otte
  • Alexander Schlaefer
Original Article

Abstract

Purpose

Estimating the interaction forces of instruments and tissue is of interest, particularly to provide haptic feedback during robot-assisted minimally invasive interventions. Different approaches based on external and integrated force sensors have been proposed. These are hampered by friction, sensor size, and sterilizability. We investigate a novel approach to estimate the force vector directly from optical coherence tomography image volumes.

Methods

We introduce a novel Siamese 3D CNN architecture. The network takes an undeformed reference volume and a deformed sample volume as an input and outputs the three components of the force vector. We employ a deep residual architecture with bottlenecks for increased efficiency. We compare the Siamese approach to methods using difference volumes and two-dimensional projections. Data were generated using a robotic setup to obtain ground-truth force vectors for silicon tissue phantoms as well as porcine tissue.

Results

Our method achieves a mean average error of \({7.7 \pm 4.3}\,{\hbox {mN}}\) when estimating the force vector. Our novel Siamese 3D CNN architecture outperforms single-path methods that achieve a mean average error of \({11.59 \pm 6.7}\,{\hbox {mN}}\). Moreover, the use of volume data leads to significantly higher performance compared to processing only surface information which achieves a mean average error of \({24.38 \pm 22.0}\,{\hbox {mN}}\). Based on the tissue dataset, our methods shows good generalization in between different subjects.

Conclusions

We propose a novel image-based force estimation method using optical coherence tomography. We illustrate that capturing the deformation of subsurface structures substantially improves force estimation. Our approach can provide accurate force estimates in surgical setups when using intraoperative optical coherence tomography.

Keywords

Force estimation OCT 3D CNN Siamese CNN 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Hamburg University of TechnologyHamburgGermany

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