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arXiv · 1804.01307

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

Anjany Sekuboyina·Markus Rempfler·Jan Kukačka·Giles Tetteh·Alexander Valentinitsch·Jan S. Kirschke·Bjoern H. Menze
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Abstract

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.

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Anjany Sekuboyina, Markus Rempfler, Jan Kukačka, Giles Tetteh, Alexander Valentinitsch, Jan S. Kirschke, Bjoern H. Menze. 2018-04-04. Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior. https://arxiv.org/abs/1804.01307

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