  Purpose: To use and test a labelling algorithm that operates on
two-dimensional (2D) reformations, rather than three-dimensional (3D) data to
locate and identify vertebrae.
  Methods: We improved the Btrfly Net (described by Sekuboyina et al) that
works on sagittal and coronal maximum intensity projections (MIP) and augmented
it with two additional components: spine-localization and adversarial a
priori-learning. Furthermore, we explored two variants of adversarial training
schemes that incorporated the anatomical a priori knowledge into the Btrfly
Net. We investigated the superiority of the proposed approach for labelling
vertebrae on three datasets: a public benchmarking dataset of 302 CT scans and
two in-house datasets with a total of 238 CT scans. We employed Wilcoxon
signed-rank test to compute the statistical significance of the improvement in
performance observed due to various architectural components in our approach.
  Results: On the public dataset, our approach using the described
Btrfly(pe-eb) network performed on par with current state-of-the-art methods
achieving a statistically significant (p < .001) vertebrae identification rate
of 88.5+/-0.2 % and localization distances of less than 7-mm. On the in-house
datasets that had a higher inter-scan data variability, we obtained an
identification rate of 85.1+/-1.2%.
  Conclusion: An identification performance comparable to existing 3D
approaches was achieved when labelling vertebrae on 2D MIPs. The performance
was further improved using the proposed adversarial training regime that
effectively enforced local spine a priori knowledge during training. Lastly,
spine-localization increased the generalizability of our approach by
homogenizing the content in the MIPs.
