A novel optical needle probe for deep learning-based tissue elasticity characterization
This work addresses tissue elasticity characterization for cancer diagnosis, but it is incremental as it builds on existing OCE probes by adding load sensing and applying deep learning.
The researchers tackled the problem of distinguishing malignant from benign tumors by developing a novel optical coherence elastography needle probe with load sensing, and they demonstrated its use with deep learning to estimate gelatin concentrations with a mean error of 1.21 ± 0.91 wt%.
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.