  The positive outcome of a trauma intervention depends on an intraoperative
evaluation of inserted metallic implants. Due to occurring metal artifacts, the
quality of this evaluation heavily depends on the performance of so-called
Metal Artifact Reduction methods (MAR). The majority of these MAR methods
require prior segmentation of the inserted metal objects. Therefore, typically
a rather simple thresholding-based segmentation method in the reconstructed 3D
volume is applied, despite some major disadvantages. With this publication, the
potential of shifting the segmentation task to a learning-based,
view-consistent 2D projection-based method on the downstream MAR's outcome is
investigated. For segmenting the present metal, a rather simple learning-based
2D projection-wise segmentation network that is trained using real data
acquired during cadaver studies, is examined. To overcome the disadvantages
that come along with a 2D projection-wise segmentation, a Consistency Filter is
proposed. The influence of the shifted segmentation domain is investigated by
comparing the results of the standard fsMAR with a modified fsMAR version using
the new segmentation masks. With a quantitative and qualitative evaluation on
real cadaver data, the investigated approach showed an increased MAR
performance and a high insensitivity against metal artifacts. For cases with
metal outside the reconstruction's FoV or cases with vanishing metal, a
significant reduction in artifacts could be shown. Thus, increases of up to
roughly 3 dB w.r.t. the mean PSNR metric over all slices and up to 9 dB for
single slices were achieved. The shown results reveal a beneficial influence of
the shift to a 2D-based segmentation method on real data for downstream use
with a MAR method, like the fsMAR.
