Computer Tomography (CT) imaging from intraoperative mobile C-Arms is commonly used to validate tool and implant placement during surgery. As a majority of tools and implants are composed of metal, physical effects such as beam hardening, photon scattering, and high absorption induce artefacts in the volume domain. These Metal Artefacts arise from a loss of signal in the projection images which is not accounted for in standard reconstruction algorithms. Metal Artefact Reduction (MAR) techniques rely on an accurate segmentation of the metal volume.,  This first segmentation step is commonly based on thresholding the volume domain which makes it prone to errors induced by metal artefacts. This thesis investigates an end-to-end trainable segmentation model which produces 3D-metal masks from 2D projection data of a 3D Cone Beam Scan of a Cios Spin System. The robustness against metal artefacts shall be evaluated and compared to common volume-domain metal segmentation approaches.