# Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle

arXiv: https://arxiv.org/abs/2110.12177 · HTML: https://mlsanity.com/paper/2110.12177/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 64 references that are in the map.
- First posted on arXiv 23 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [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)
- [Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization](https://mlsanity.com/paper/1705.05998/) (2017)
- [Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks](https://mlsanity.com/paper/1712.01509/) (2017)
- [Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior](https://mlsanity.com/paper/1804.01307/) (2018)
- [Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization](https://mlsanity.com/paper/2012.07947/) (2020)
- [Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information](https://mlsanity.com/paper/1812.03500/) (2018)
- [Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks](https://mlsanity.com/paper/2009.13798/) (2020)
- [A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets](https://mlsanity.com/paper/1703.04347/) (2017)
- [Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling](https://mlsanity.com/paper/2001.00170/) (2020)
- [Vertebrae Detection and Localization in CT with Two-Stage CNNs and Dense Annotations](https://mlsanity.com/paper/1910.05911/) (2019)

## Built on by (most cited first, 8 of 8)

- [Multi-View Vertebra Localization and Identification from CT Images](https://mlsanity.com/paper/2307.12845/) (2023)
- [Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration](https://mlsanity.com/paper/2305.06252/) (2023)
- [SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation](https://mlsanity.com/paper/2407.08555/) (2024)
- [Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks](https://mlsanity.com/paper/2308.02509/) (2023)
- [Unified Medical Image Segmentation with State Space Modeling Snake](https://mlsanity.com/paper/2507.12760/) (2025)
- [Towards Markerless Intraoperative Tracking of Deformable Spine Tissue](https://mlsanity.com/paper/2506.23657/) (2025)
- [VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences](https://mlsanity.com/paper/2601.14066/) (2026)
- [VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray](https://mlsanity.com/paper/2302.03476/) (2023)

## Abstract (arXiv)

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.