# Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization

arXiv: https://arxiv.org/abs/2204.06818 · HTML: https://mlsanity.com/paper/2204.06818/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning](https://mlsanity.com/paper/1802.09232/) (2018)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Human Pose Regression by Combining Indirect Part Detection and Contextual Information](https://mlsanity.com/paper/1710.02322/) (2017)
- [A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI](https://mlsanity.com/paper/2007.02606/) (2020)
- [Detection of vertebral fractures in CT using 3D Convolutional Neural Networks](https://mlsanity.com/paper/1911.01816/) (2019)
- [Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification](https://mlsanity.com/paper/2005.11960/) (2020)
- [Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection](https://mlsanity.com/paper/2008.07831/) (2020)
- [Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks](https://mlsanity.com/paper/2009.13798/) (2020)
- [Compression Fractures Detection on CT](https://mlsanity.com/paper/1706.01671/) (2017)
- [3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT](https://mlsanity.com/paper/2010.03739/) (2020)
- [Deep convolutional networks for automated detection of posterior-element fractures on spine CT](https://mlsanity.com/paper/1602.00020/) (2016)

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

- [Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder](https://mlsanity.com/paper/2408.01571/) (2024)
- [Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading](https://mlsanity.com/paper/2303.12031/) (2023)
- [Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding](https://mlsanity.com/paper/2312.05220/) (2023)
- [Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification](https://mlsanity.com/paper/2309.06825/) (2023)
- [Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes](https://mlsanity.com/paper/2404.02830/) (2024)
- [Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs Segmentation](https://mlsanity.com/paper/2405.15500/) (2024)
- [Robust Curve Detection in Volumetric Medical Imaging via Attraction Field](https://mlsanity.com/paper/2408.01159/) (2024)
- [HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture Grading](https://mlsanity.com/paper/2503.05990/) (2025)

## Abstract (arXiv)

Vertebral body compression fractures are 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 detect individual vertebrae and quantify fractures in 2D simultaneously. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to the current clinical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an interpretable 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 and recall are 0.99), and fracture identification (ROC AUC at the patient level is up to 0.96). Our anchor-free vertebra detection network shows excellent generalizability on a new domain by achieving ROC AUC 0.95, sensitivity 0.85, specificity 0.9 on a challenging VerSe dataset with many unseen vertebra types.