Publisher DOI: 10.1007/978-3-030-00937-3_26
Title: Needle Tip Force Estimation Using an OCT Fiber and a Fused convGRU-CNN Architecture
Language: English
Authors: Gessert, Nils 
Priegnitz, Torben 
Saathoff, Thore 
Antoni, Sven-Thomas 
Meyer, David 
Hamann, Moritz Franz 
Jünemann, Klaus Peter 
Otte, Christoph 
Schlaefer, Alexander 
Issue Date: 2018
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (11073 LNCS): 222-229 (2018)
Journal or Series Name: Lecture notes in computer science 
Abstract (english): Needle insertion is common during minimally invasive interventions such as biopsy or brachytherapy. During soft tissue needle insertion, forces acting at the needle tip cause tissue deformation and needle deflection. Accurate needle tip force measurement provides information on needle-tissue interaction and helps detecting and compensating potential misplacement. For this purpose we introduce an image-based needle tip force estimation method using an optical fiber imaging the deformation of an epoxy layer below the needle tip over time. For calibration and force estimation, we introduce a novel deep learning-based fused convolutional GRU-CNN model which effectively exploits the spatio-temporal data structure. The needle is easy to manufacture and our model achieves a mean absolute error of 1.76±1.5 mN with a cross-correlation coefficient of 0.9996, clearly outperforming other methods. We test needles with different materials to demonstrate that the approach can be adapted for different sensitivities and force ranges. Furthermore, we validate our approach in an ex-vivo prostate needle insertion scenario.
ISBN: 978-303000936-6
ISSN: 0302-9743
Institute: Medizintechnische Systeme E-1 
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
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