# Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization

arXiv: https://arxiv.org/abs/2012.07947 · HTML: https://mlsanity.com/paper/2012.07947/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.00673/) (2023)
- [Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle](https://mlsanity.com/paper/2110.12177/) (2021)
- [Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding](https://mlsanity.com/paper/2212.02014/) (2022)
- [Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations](https://mlsanity.com/paper/2410.16057/) (2024)
- [VertMatch: A Semi-supervised Framework for Vertebral Structure Detection in 3D Ultrasound Volume](https://mlsanity.com/paper/2212.14747/) (2022)
- [Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification](https://mlsanity.com/paper/2309.06825/) (2023)
- [Robust Curve Detection in Volumetric Medical Imaging via Attraction Field](https://mlsanity.com/paper/2408.01159/) (2024)
- [Dual Teacher-Student Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.11018/) (2025)
- [JanusNet: Hierarchical Slice-Block Shuffle and Displacement for Semi-Supervised 3D Multi-Organ Segmentation](https://mlsanity.com/paper/2508.03997/) (2025)

## Abstract (arXiv)

Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are posed in this task by highly varying pathologies (such as vertebral compression fracture, scoliosis, and vertebral fixation) and imaging conditions (such as limited field of view and metal streak artifacts). This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification. A key point localization model is trained to produce activation maps of vertebra centers. They are then re-sampled along the spine centerline to produce spine-rectified activation maps, which are further aggregated into 1-D activation signals. Following this, an anatomically-constrained optimization module is introduced to jointly search for the optimal vertebra centers under a soft constraint that regulates the distance between vertebrae and a hard constraint on the consecutive vertebra indices. When being evaluated on a major public benchmark of 302 highly pathological CT images, the proposed method reports the state of the art identification (id.) rate of 97.4%, and outperforms the best competing method of 94.7% id. rate by reducing the relative id. error rate by half.