Needle Tip Force Estimation Using an OCT Fiber and a Fused convGRU-CNN Architecture

First published in
Lecture notes in computer science 
Number in series
11073 LNCS
Start Page
222
End Page
229
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (11073 LNCS): 222-229 (2018)
Contribution to Conference
International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 
Publisher DOI
10.1007/978-3-030-00937-3_26
Scopus ID
2-s2.0-85053826079
Publisher
Springer
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.
DDC Class
600: Technik
610: Medizin