A novel optical needle probe for deep learning-based tissue elasticity characterization

The distinction between malignant and benign tumors is essential to the treatment of cancer. The tissue's elasticity can be used as an indicator for the required tissue characterization. Optical coherence elastography (OCE) probes have been proposed for needle insertions but have so far lacked the necessary load sensing capabilities. We present a novel OCE needle probe that provides simultaneous optical coherence tomography (OCT) imaging and load sensing at the needle tip. We demonstrate the application of the needle probe in indentation experiments on gelatin phantoms with varying gelatin concentrations. We further implement two deep learning methods for the end-toend sample characterization from the acquired OCT data. We report the estimation of gelatin sample weight ratios [wt%] in unseen samples with a mean error of 1.21 ± 0.91 wt%. Both evaluated deep learning models successfully provide sample characterization with different advantages regarding the accuracy and inference time.

Subjects

Deep Learning

Elastography

Needle Probe

Optical Coherence Tomography

Tissue Characterization

DDC Class

600: Technik

610: Medizin

Funding(s)

Mechatronisch geführte Mikronavigation von Nadeln in Weichgewebe

Funding Organisations

Deutsche Forschungsgemeinschaft (DFG)

More Funding Information

This work was partially funded by the TUHH i3 initiative.

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

10.1515_cdbme-2021-1005.pdf

Size

1.23 MB

Format

Adobe PDF

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A novel optical needle probe for deep learning-based tissue elasticity characterization