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Abstract: 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 deformationof an epoxy layer below the needle tip over time. For calibration andforce estimation, we introduce a novel deep learning\-based fused convolutionalGRU\-CNN model which effectively exploits the spatio\-temporaldata structure. The needle is easy to manufacture and our model achieves a mean absolute error of 1\.76 \± 1\.50 mN with a cross\-correlation coefficientof 0\.9996, clearly outperforming other methods. We test needleswith different materials to demonstrate that the approach can be adaptedfor different sensitivities and force ranges. Furthermore, we validate our approach in an ex\-vivo prostate needle insertion scenario