Spatio-temporal deep learning models for tip force estimation during needle insertion-Reference-Cited by-同舟云学术

Spatio-temporal deep learning models for tip force estimation during needle insertion

Published:2019-05-30 Issue:9 Volume:14 Page:1485-1493
ISSN:1861-6410
Container-title:International Journal of Computer Assisted Radiology and Surgery
language:en
Short-container-title:Int J CARS

Author:

Gessert Nils^ORCID,Priegnitz Torben,Saathoff Thore,Antoni Sven-Thomas,Meyer David,Hamann Moritz Franz,Jünemann Klaus-Peter,Otte Christoph,Schlaefer Alexander

Funder

Deutsche Forschungsgemeinschaft

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology, Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11548-019-02006-z.pdf

Reference39 articles.

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2. Taylor RH, Menciassi A, Fichtinger G, Fiorini P, Dario P (2016) Medical robotics and computer-integrated surgery. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer Handbooks. Springer, Cham

3. Okamura AM, Simone C, O’leary MD (2004) Force modeling for needle insertion into soft tissue. IEEE Trans Biomed Eng 51(10):1707–1716

4. Kataoka H, Washio T, Chinzei K, Mizuhara K, Simone C, Okamura AM (2002) Measurement of the tip and friction force acting on a needle during penetration. In: International conference on medical image computing and computer-assisted intervention, pp 216–223

5. Rodrigues S, Horeman T, Sam P, Dankelman J, van den Dobbelsteen J, Jansen FW (2014) Influence of visual force feedback on tissue handling in minimally invasive surgery. British J Surg 101(13):1766–1773

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