@article{MielingSprengerLatusBargstenSchlaefer+2021+21+25,
author = {R. Mieling and J. Sprenger and S. Latus and L. Bargsten and A. Schlaefer},
title = {A novel optical needle probe for deep learning-based tissue elasticity characterization:.},
journal = {Current Directions in Biomedical Engineering.},
year = {2021},
volume = {7.},
number = {(1),},
pages = {21-25},
doi = {doi:10.1515/cdbme-2021-1005},
url = {https://doi.org/10.1515/cdbme-2021-1005},
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 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}
}

@COMMENT{Bibtex file generated on 2026-5-28 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications/2024-2020 }