|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
Hamann, Moritz Franz
Jünemann, Klaus Peter
|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.||URI:||http://hdl.handle.net/11420/2791||ISBN:||978-303000936-6||ISSN:||0302-9743||Institute:||Medizintechnische Systeme E-1||Type:||InProceedings (Aufsatz / Paper einer Konferenz etc.)|
|Appears in Collections:||Publications without fulltext|
Show full item record
checked on Jul 22, 2019
Items in TORE are protected by copyright, with all rights reserved, unless otherwise indicated.