  Robust localisation and identification of vertebrae is essential for
automated spine analysis. The contribution of this work to the task is
two-fold: (1) Inspired by the human expert, we hypothesise that a sagittal and
coronal reformation of the spine contain sufficient information for labelling
the vertebrae. Thereby, we propose a butterfly-shaped network architecture
(termed Btrfly Net) that efficiently combines the information across
reformations. (2) Underpinning the Btrfly net, we present an energy-based
adversarial training regime that encodes local spine structure as an anatomical
prior into the network, thereby enabling it to achieve state-of-art performance
in all standard metrics on a benchmark dataset of 302 scans without any
post-processing during inference.
