Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatio-Temporal Deep Learning Models for Tip Force Estimation During
  Needle Insertion   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.

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The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
2
The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc