# Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification

arXiv: https://arxiv.org/abs/2005.11960 · HTML: https://mlsanity.com/paper/2005.11960/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

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

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder](https://mlsanity.com/paper/2408.01571/) (2024)
- [Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization](https://mlsanity.com/paper/2204.06818/) (2022)
- [Interpretable Vertebral Fracture Diagnosis](https://mlsanity.com/paper/2203.16273/) (2022)
- [Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection -- Towards Precise Fish Morphological Assessment in Aquaculture Breeding](https://mlsanity.com/paper/2405.12476/) (2024)
- [Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading](https://mlsanity.com/paper/2303.12031/) (2023)
- [Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision](https://mlsanity.com/paper/2409.03261/) (2024)
- [Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes](https://mlsanity.com/paper/2404.02830/) (2024)

## Abstract (arXiv)

Vertebral body compression fractures are reliable early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of vertebral fracture classification proves its reliable quality; however, existing methods provide hard-to-interpret outputs and sometimes fail to process cases with severe abnormalities such as highly pathological vertebrae or scoliosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then to simultaneously detect individual vertebrae and quantify fractures in 2D. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to current medical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an intuitive and verifiable output. The method approaches expert-level performance and demonstrates state-of-the-art results in vertebrae 3D localization (the average error is 1 mm), vertebrae 2D detection (precision is 0.99, recall is 1), and fracture identification (ROC AUC at the patient level is 0.93).