  Accurately localizing and identifying vertebrae from CT images is crucial for
various clinical applications. However, most existing efforts are performed on
3D with cropping patch operation, suffering from the large computation costs
and limited global information. In this paper, we propose a multi-view vertebra
localization and identification from CT images, converting the 3D problem into
a 2D localization and identification task on different views. Without the
limitation of the 3D cropped patch, our method can learn the multi-view global
information naturally. Moreover, to better capture the anatomical structure
information from different view perspectives, a multi-view contrastive learning
strategy is developed to pre-train the backbone. Additionally, we further
propose a Sequence Loss to maintain the sequential structure embedded along the
vertebrae. Evaluation results demonstrate that, with only two 2D networks, our
method can localize and identify vertebrae in CT images accurately, and
outperforms the state-of-the-art methods consistently. Our code is available at
https://github.com/ShanghaiTech-IMPACT/Multi-View-Vertebra-Localization-and-Identification-from-CT-Images.
