Robust vertebra identification using simultaneous node and edge
  predicting Graph Neural Networks
Vincent Bürgin Raphael Prevost Marijn F. Stollenga
Automatic vertebra localization and identification in CT scans is important
for numerous clinical applications. Much progress has been made on this topic,
but it mostly targets positional localization of vertebrae, ignoring their
orientation. Additionally, most methods employ heuristics in their pipeline
that can be sensitive in real clinical images which tend to contain
abnormalities. We introduce a simple pipeline that employs a standard
prediction with a U-Net, followed by a single graph neural network to associate
and classify vertebrae with full orientation. To test our method, we introduce
a new vertebra dataset that also contains pedicle detections that are
associated with vertebra bodies, creating a more challenging landmark
prediction, association and classification task. Our method is able to
accurately associate the correct body and pedicle landmarks, ignore false
positives and classify vertebrae in a simple, fully trainable pipeline avoiding
application-specific heuristics. We show our method outperforms traditional
approaches such as Hungarian Matching and Hidden Markov Models. We also show
competitive performance on the standard VerSe challenge body identification
task.