  The aim of this study is to investigate the segmentation accuracies of
different segmentation networks trained on 730 manually annotated lateral
lumbar spine X-rays. Instance segmentation networks were compared to semantic
segmentation networks. The study cohort comprised diseased spines and
postoperative images with metallic implants. The average mean accuracy and mean
intersection over union (IoU) was up to 3 percent better for the best
performing instance segmentation model, the average pixel accuracy and weighted
IoU were slightly better for the best performing semantic segmentation model.
Moreover, the inferences of the instance segmentation models are easier to
implement for further processing pipelines in clinical decision support.
