Anzeige
Erschienen in:
04.05.2018 | Original Article
Force estimation from OCT volumes using 3D CNNs
Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 7/2018
EinloggenAktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.