  Metal implants that are inserted into the patient's body during trauma
interventions cause heavy artifacts in 3D X-ray acquisitions. Metal Artifact
Reduction (MAR) methods, whose first step is always a segmentation of the
present metal objects, try to remove these artifacts. Thereby, the segmentation
is a crucial task which has strong influence on the MAR's outcome. This study
proposes and evaluates a learning-based patch-wise segmentation network and a
newly proposed Consistency Check as post-processing step. The combination of
the learned segmentation and Consistency Check reaches a high segmentation
performance with an average IoU score of 0.924 on the test set. Furthermore,
the Consistency Check proves the ability to significantly reduce false positive
segmentations whilst simultaneously ensuring consistent segmentations.
