# Vertebra-Focused Landmark Detection for Scoliosis Assessment

arXiv: https://arxiv.org/abs/2001.03187 · HTML: https://mlsanity.com/paper/2001.03187/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression](https://mlsanity.com/paper/2208.12462/) (2022)
- [Morphology-Aware Interactive Keypoint Estimation](https://mlsanity.com/paper/2209.07163/) (2022)
- [Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph](https://mlsanity.com/paper/2104.14629/) (2021)
- [Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs](https://mlsanity.com/paper/2206.02087/) (2022)
- [CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization](https://mlsanity.com/paper/2411.17845/) (2024)
- [Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation](https://mlsanity.com/paper/2211.14122/) (2022)
- [Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification](https://mlsanity.com/paper/2309.06825/) (2023)
- [Deep learning automates Cobb angle measurement compared with multi-expert observers](https://mlsanity.com/paper/2403.12115/) (2024)
- [CurvNet: Latent Contour Representation and Iterative Data Engine for Curvature Angle Estimation](https://mlsanity.com/paper/2411.12604/) (2024)
- [VerteNet -- A Multi-Context Hybrid CNN Transformer for Accurate Vertebral Landmark Localization in Lateral Spine DXA Images](https://mlsanity.com/paper/2502.02097/) (2025)
- [Submillimeter-Accurate 3D Lumbar Spine Reconstruction from Biplanar X-Ray Images: Incorporating a Multi-Task Network and Landmark-Weighted Loss](https://mlsanity.com/paper/2503.14573/) (2025)

## Abstract (arXiv)

Adolescent idiopathic scoliosis (AIS) is a lifetime disease that arises in children. Accurate estimation of Cobb angles of the scoliosis is essential for clinicians to make diagnosis and treatment decisions. The Cobb angles are measured according to the vertebrae landmarks. Existing regression-based methods for the vertebra landmark detection typically suffer from large dense mapping parameters and inaccurate landmark localization. The segmentation-based methods tend to predict connected or corrupted vertebra masks. In this paper, we propose a novel vertebra-focused landmark detection method. Our model first localizes the vertebra centers, based on which it then traces the four corner landmarks of the vertebra through the learned corner offset. In this way, our method is able to keep the order of the landmarks. The comparison results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images. Code is available at: urlhttps://github.com/yijingru/Vertebra-Landmark-Detection.