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

Robust localisation and identification of vertebrae is an essential part of 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 butterflyshaped network architecture (termed Btrfly Net) that efficiently combines the information across the reformations. (2) Underpinning the Btrfly net, we present an energy-based adversarial training regime that encodes the 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.