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Publisher DOI: 10.1515/cdbme-2021-1005
Title: A novel optical needle probe for deep learning-based tissue elasticity characterization
Language: English
Authors: Mieling, Till Robin 
Sprenger, Johanna 
Latus, Sarah 
Holstein, Lennart 
Schlaefer, Alexander 
Keywords: Deep Learning;Elastography;Needle Probe;Optical Coherence Tomography;Tissue Characterization
Issue Date: 1-Aug-2021
Publisher: De Gruyter
Source: Current Directions in Biomedical Engineering 7 (1): 20211105, 21-25 (2021-08-01)
Journal: Current directions in biomedical engineering 
Abstract (english): 
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.
DOI: 10.15480/882.3823
ISSN: 2364-5504
Institute: Medizintechnische und Intelligente Systeme E-1 
Document Type: Article
Funded by: Deutsche Forschungsgemeinschaft (DFG) 
More Funding information: This work was partially funded by the TUHH i3 initiative.
Project: Mechatronisch geführte Mikronavigation von Nadeln in Weichgewebe 
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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