Options
A novel optical needle probe for deep learning-based tissue elasticity characterization
Citation Link: https://doi.org/10.15480/882.3823
Publikationstyp
Journal Article
Publikationsdatum
2021-08-01
Sprache
English
Author
Enthalten in
Volume
7
Issue
1
Start Page
21
End Page
25
Article Number
20211105
Citation
Current Directions in Biomedical Engineering 7 (1): 20211105, 21-25 (2021-08-01)
Publisher DOI
Scopus ID
Publisher
De Gruyter
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.
Schlagworte
Deep Learning
Elastography
Needle Probe
Optical Coherence Tomography
Tissue Characterization
DDC Class
600: Technik
610: Medizin
Funding Organisations
More Funding Information
This work was partially funded by the TUHH i3 initiative.
Publication version
publishedVersion
Loading...
Name
10.1515_cdbme-2021-1005.pdf
Size
1.23 MB
Format
Adobe PDF