  In several image acquisition and processing steps of X-ray radiography,
knowledge of the existence of metal implants and their exact position is highly
beneficial (e.g. dose regulation, image contrast adjustment). Another
application which would benefit from an accurate metal segmentation is cone
beam computed tomography (CBCT) which is based on 2D X-ray projections. Due to
the high attenuation of metals, severe artifacts occur in the 3D X-ray
acquisitions. The metal segmentation in CBCT projections usually serves as a
prerequisite for metal artifact avoidance and reduction algorithms. Since the
generation of high quality clinical training is a constant challenge, this
study proposes to generate simulated X-ray images based on CT data sets
combined with self-designed computer aided design (CAD) implants and make use
of convolutional neural network (CNN) and vision transformer (ViT) for metal
segmentation. Model test is performed on accurately labeled X-ray test datasets
obtained from specimen scans. The CNN encoder-based network like U-Net has
limited performance on cadaver test data with an average dice score below 0.30,
while the metal segmentation transformer with dual decoder (MST-DD) shows high
robustness and generalization on the segmentation task, with an average dice
score of 0.90. Our study indicates that the CAD model-based data generation has
high flexibility and could be a way to overcome the problem of shortage in
clinical data sampling and labelling. Furthermore, the MST-DD approach
generates a more reliable neural network in case of training on simulated data.
