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Robust localisation and identification of vertebrae is essential forautomated spine analysis. The contribution of this work to the task istwo-fold: (1) Inspired by the human expert, we hypothesise that a sagittal andcoronal reformation of the spine contain sufficient information for labellingthe vertebrae. Thereby, we propose a butterfly-shaped network architecture(termed Btrfly Net) that efficiently combines the information acrossreformations. (2) Underpinning the Btrfly net, we present an energy-basedadversarial training regime that encodes local spine structure as an anatomicalprior into the network, thereby enabling it to achieve state-of-art performancein all standard metrics on a benchmark dataset of 302 scans without anypost-processing during inference.

Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior