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.
CITATION STYLE
Sekuboyina, A., Rempfler, M., Kukačka, J., Tetteh, G., Valentinitsch, A., Kirschke, J. S., & Menze, B. H. (2018). Btrfly Net: Vertebrae Labelling with Energy-Based Adversarial Learning of Local Spine Prior. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11073 LNCS, pp. 649–657). Springer Verlag. https://doi.org/10.1007/978-3-030-00937-3_74
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