  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.
