  Vertebrae identification in arbitrary fields-of-view plays a crucial role in
diagnosing spine disease. Most spine CT contain only local regions, such as the
neck, chest, and abdomen. Therefore, identification should not depend on
specific vertebrae or a particular number of vertebrae being visible. Existing
methods at the spine-level are unable to meet this challenge. In this paper, we
propose a three-stage method to address the challenges in 3D CT vertebrae
identification at vertebrae-level. By sequentially performing the tasks of
vertebrae localization, segmentation, and identification, the anatomical prior
information of the vertebrae is effectively utilized throughout the process.
Specifically, we introduce a dual-factor density clustering algorithm to
acquire localization information for individual vertebra, thereby facilitating
subsequent segmentation and identification processes. In addition, to tackle
the issue of interclass similarity and intra-class variability, we pre-train
our identification network by using a supervised contrastive learning method.
To further optimize the identification results, we estimated the uncertainty of
the classification network and utilized the message fusion module to combine
the uncertainty scores, while aggregating global information about the spine.
Our method achieves state-of-the-art results on the VerSe19 and VerSe20
challenge benchmarks. Additionally, our approach demonstrates outstanding
generalization performance on an collected dataset containing a wide range of
abnormal cases.
