The objective of this paper is to provide a stepforward towards the per procedural visualisation of the electric field distribution during a clinical irreversible electroporation (IRE) procedure. To this end, an automated workflow is needed to compute the electric field distribution on a single Cone Beam Computed Tomography (CBCT) scan. The aim of the current paper is to propose a deep learning strategy for the automatic segmentation of the needles. In particular, a novel coarse-to-fine approach is proposed to extract relevant needle information from the CBCT scan, despite inherent artefacts generated during capture. The obtained needle information is subsequently fed into a standard static linear model for the electric field computation. Since the setup is performed in the medical image framework, the electric field distribution and the region of interest are visible to provide to the radiologist a visual evaluation of the treatment. The segmentation results are evaluated on 8 of the 16 patients of the dataset using the Dice coefficient to compare the predicted segmentation with the ground truth. The proposed segmentation method is fast (around 2 minutes are needed with a commodity hardware), allowing its use in a clinical setting.Read less <