Needle tip force estimation by deep learning from raw spectral OCT data.

Journal: International journal of computer assisted radiology and surgery

Published Date: Jul 22, 2020

Abstract

PURPOSE: Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be used to image tissue. Here, we study how to calibrate OCT to sense forces, e.g., during robotic needle placement.

Authors

M Gromniak

Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany. martin.gromniak@tuhh.de.
N Gessert

Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
T Saathoff

Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
A Schlaefer

Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.

Keywords

Calibration Deep Learning Feedback Humans Image Processing, Computer-Assisted Mechanical Phenomena Needles Neural Networks, Computer Robotics Tomography, Optical Coherence

External Resources

View on PubMed Access via DOI PubMed (32700243)

Needle tip force estimation by deep learning from raw spectral OCT data.

Abstract

Authors

Keywords

External Resources

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