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Force estimation from OCT volumes using 3D CNNs.

Nils Gessert1, Jens Beringhoff2, Christoph Otte2

  • 1Hamburg University of Technology, Schwarzenbergstraße 95, 21073, Hamburg, Germany. nils.gessert@tuhh.de.

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|May 6, 2018
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Summary
This summary is machine-generated.

This study introduces a new method using optical coherence tomography (OCT) to estimate instrument-tissue interaction forces for robot-assisted surgery. The novel approach accurately captures tissue deformation, improving force estimation for enhanced haptic feedback.

Keywords:
3D CNNForce estimationOCTSiamese CNN

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Area of Science:

  • Medical Robotics
  • Surgical Technology
  • Biomedical Imaging

Background:

  • Estimating instrument-tissue interaction forces is crucial for haptic feedback in robot-assisted minimally invasive surgery.
  • Existing force sensing methods face limitations such as friction, sensor size, and sterilizability issues.

Purpose of the Study:

  • To investigate a novel approach for estimating the force vector directly from optical coherence tomography (OCT) image volumes.
  • To develop an image-based method for accurate force estimation in surgical interventions.

Main Methods:

  • Introduction of a novel Siamese 3D Convolutional Neural Network (CNN) architecture.
  • The network processes undeformed reference and deformed sample OCT volumes to output the force vector components.
  • Comparison with difference volume and 2D projection methods using robotic setup data from tissue phantoms and porcine tissue.

Main Results:

  • The proposed Siamese 3D CNN method achieved a mean average error of [Formula: see text] for force vector estimation.
  • This novel architecture outperformed single-path methods ([Formula: see text] MAE) and surface-only processing ([Formula: see text] MAE).
  • The method demonstrated good generalization across different subjects in the tissue dataset.

Conclusions:

  • A novel image-based force estimation method using OCT is proposed.
  • Capturing deformation of subsurface structures significantly enhances force estimation accuracy.
  • The approach offers accurate force estimates for intraoperative use in surgical setups.