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

• Published in: International journal for computer assisted radiology and surgery 2019-09, Vol.14 (9), p.1485-1493
• Main Authors: Gessert, Nils, Priegnitz, Torben, Saathoff, Thore, Antoni, Sven-Thomas, Meyer, David, Hamann, Moritz Franz, Jünemann, Klaus-Peter, Otte, Christoph, Schlaefer, Alexander
• Format: Article
• Language: English
• Subjects: Algorithms; Architecture; Biopsy - instrumentation; Biopsy - methods; Brachytherapy - instrumentation; Brachytherapy - methods; Calibration; Computer Imaging; Computer Science; Correlation coefficients; Data processing; Deep Learning; Deformation effects; Deformation mechanisms; Depth profiling; Equipment Design; Fiber optics; Health Informatics; Humans; Imaging; Machine learning; Mechanical Phenomena; Medicine; Medicine & Public Health; Needles; Optical fibers; Original; Original Article; Pattern Recognition and Graphics; Prostate; Radiation therapy; Radiology; Spatial data; Stiffness; Surgery; Tomography, Optical Coherence; Vision
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-019-02006-z

Purpose 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. Methods We describe a fiber-optic needle tip force sensor design using a single OCT fiber for measurement. The fiber images the deformation of an epoxy layer placed below the needle tip which results in a stream of 1D depth profiles. We study different deep learning approaches to facilitate calibration between this spatio-temporal image data and the related forces. In particular, we propose a novel convGRU-CNN architecture for simultaneous spatial and temporal data processing. Results The needle can be adapted to different operating ranges by changing the stiffness of the epoxy layer. Likewise, calibration can be adapted by training the deep learning models. Our novel convGRU-CNN architecture results in the lowest mean absolute error of 1.59 ± 1.3 mN and a cross-correlation coefficient of 0.9997 and clearly outperforms the other methods. Ex vivo experiments in human prostate tissue demonstrate the needle’s application. Conclusions Our OCT-based fiber-optic sensor presents a viable alternative for needle tip force estimation. The results indicate that the rich spatio-temporal information included in the stream of images showing the deformation throughout the epoxy layer can be effectively used by deep learning models. Particularly, we demonstrate that the convGRU-CNN architecture performs favorably, making it a promising approach for other spatio-temporal learning problems.