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Vertebrae identification in arbitrary fields-of-view plays a crucial role indiagnosing spine disease. Most spine CT contain only local regions, such as theneck, chest, and abdomen. Therefore, identification should not depend onspecific vertebrae or a particular number of vertebrae being visible. Existingmethods at the spine-level are unable to meet this challenge. In this paper, wepropose a three-stage method to address the challenges in 3D CT vertebraeidentification at vertebrae-level. By sequentially performing the tasks ofvertebrae localization, segmentation, and identification, the anatomical priorinformation of the vertebrae is effectively utilized throughout the process.Specifically, we introduce a dual-factor density clustering algorithm toacquire localization information for individual vertebra, thereby facilitatingsubsequent segmentation and identification processes. In addition, to tacklethe issue of interclass similarity and intra-class variability, we pre-trainour identification network by using a supervised contrastive learning method.To further optimize the identification results, we estimated the uncertainty ofthe classification network and utilized the message fusion module to combinethe uncertainty scores, while aggregating global information about the spine.Our method achieves state-of-the-art results on the VerSe19 and VerSe20challenge benchmarks. Additionally, our approach demonstrates outstandinggeneralization performance on an collected dataset containing a wide range ofabnormal cases.

SpineCLUE: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation