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

Autor: David Meyer, Sven-Thomas Antoni, K.P. Jünemann, Nils Gessert, Torben Priegnitz, Thore Saathoff, Christoph Otte, Moritz Franz Hamann, Alexander Schlaefer
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Computer science
Biopsy
Computer Vision and Pattern Recognition (cs.CV)
Brachytherapy
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Convolutional neural network
030218 nuclear medicine & medical imaging
0302 clinical medicine
medicine.diagnostic_test
Image and Video Processing (eess.IV)
Stiffness
Equipment Design
General Medicine
Computer Graphics and Computer-Aided Design
Computer Science Applications
Temporal database
Needles
Calibration
Original Article
Needle insertion
Computer Vision and Pattern Recognition
medicine.symptom
Needle placement
Algorithms
Tomography
Optical Coherence
Acoustics
0206 medical engineering
Biomedical Engineering
Health Informatics
Deformation (meteorology)
03 medical and health sciences
Deep Learning
Optical coherence tomography
FOS: Electrical engineering
electronic engineering
information engineering
medicine
Humans
Radiology
Nuclear Medicine and imaging
Convolution neural network
Mechanical Phenomena
business.industry
Deep learning
Electrical Engineering and Systems Science - Image and Video Processing
020601 biomedical engineering
Convolutional GRU
Surgery
Force estimation
Artificial intelligence
business
Zdroj: International Journal of Computer Assisted Radiology and Surgery
ISSN: 1861-6429
1861-6410
DOI: 10.1007/s11548-019-02006-z
Popis: 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-optical 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-optical 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.
Accepted for publication in the International Journal of Computer Assisted Radiology and Surgery
Databáze: OpenAIRE
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