@article{Gessert2019,
author = {N. Gessert and T. Priegnitz and T. Saathoff and S.-T. Antoni and D. Meyer and  M. F. Hamann and K.-P. Jünemann and  C. Otte and A. Schlaefer},
title = {Spatio-temporal deep learning models for tip force estimation during needle insertion.},
journal = {International Journal of Computer Assisted Radiology and Surgery.},
year = {2019},
month = {May},
doi = {10.1007/s11548-019-02006-z},
url = {https://doi.org/10.1007/s11548-019-02006-z},
abstract = {Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. Hence, a number of approaches to estimate the forces at the needle have been proposed. Yet, integrating sensors into the needle tip is challenging and a careful calibration is required to obtain good force estimates.}
}

@COMMENT{Bibtex file generated on 2026-7-1 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications/2019-2013 }