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

TL;DR

This paper introduces a novel optical needle probe combining OCT imaging and load sensing for tissue elasticity assessment, utilizing deep learning for accurate tissue characterization in phantom experiments.

Contribution

The paper presents a new OCE needle probe with load sensing capabilities and demonstrates deep learning methods for end-to-end tissue characterization from OCT data.

Findings

Successful gelatin concentration estimation with mean error of 1.21 wt%.

Deep learning models achieved accurate tissue characterization.

The probe enables simultaneous imaging and load sensing during tissue analysis.

Abstract

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-to-end sample characterization from the acquired OCT data. We report the estimation of gelatin sample concentrations in unseen samples with a mean error of $1.21 \pm 0.91$ wt\%. Both evaluated deep learning models successfully provide sample characterization with different advantages regarding the accuracy and inference time.