  Vertebrae localization, segmentation and identification in CT images is key
to numerous clinical applications. While deep learning strategies have brought
to this field significant improvements over recent years, transitional and
pathological vertebrae are still plaguing most existing approaches as a
consequence of their poor representation in training datasets. Alternatively,
proposed non-learning based methods take benefit of prior knowledge to handle
such particular cases. In this work we propose to combine both strategies. To
this purpose we introduce an iterative cycle in which individual vertebrae are
recursively localized, segmented and identified using deep-networks, while
anatomic consistency is enforced using statistical priors. In this strategy,
the transitional vertebrae identification is handled by encoding their
configurations in a graphical model that aggregates local deep-network
predictions into an anatomically consistent final result. Our approach achieves
state-of-the-art results on the VerSe20 challenge benchmark, and outperforms
all methods on transitional vertebrae as well as the generalization to the
VerSe19 challenge benchmark. Furthermore, our method can detect and report
inconsistent spine regions that do not satisfy the anatomic consistency priors.
Our code and model are openly available for research purposes.
