# Multi-View Vertebra Localization and Identification from CT Images

arXiv: https://arxiv.org/abs/2307.12845 · HTML: https://mlsanity.com/paper/2307.12845/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Iterative fully convolutional neural networks for automatic vertebra segmentation and identification](https://mlsanity.com/paper/1804.04383/) (2018)
- [Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior](https://mlsanity.com/paper/1804.01307/) (2018)
- [Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle](https://mlsanity.com/paper/2110.12177/) (2021)
- [Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks](https://mlsanity.com/paper/2009.13798/) (2020)

## Built on by (most cited first, 7 of 7)

- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation](https://mlsanity.com/paper/2407.08555/) (2024)
- [MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention](https://mlsanity.com/paper/2507.02488/) (2025)
- [Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data](https://mlsanity.com/paper/2401.16027/) (2024)
- [Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification](https://mlsanity.com/paper/2407.02926/) (2024)
- [Frequency-enhanced Multi-granularity Context Network for Efficient Vertebrae Segmentation](https://mlsanity.com/paper/2506.23086/) (2025)
- [Self-Supervised Multiview Xray Matching](https://mlsanity.com/paper/2507.00287/) (2025)

## Abstract (arXiv)

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.