# Detection of vertebral fractures in CT using 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1911.01816 · HTML: https://mlsanity.com/paper/1911.01816/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

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

- [Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization](https://mlsanity.com/paper/2204.06818/) (2022)
- [Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning](https://mlsanity.com/paper/2208.10698/) (2022)
- [Interpretable Vertebral Fracture Diagnosis](https://mlsanity.com/paper/2203.16273/) (2022)
- [Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding](https://mlsanity.com/paper/2312.05220/) (2023)
- [Scoliosis Detection using Deep Neural Network](https://mlsanity.com/paper/2210.17269/) (2022)
- [Intelligent Scoliosis Screening and Diagnosis: A Survey](https://mlsanity.com/paper/2310.08756/) (2023)
- [Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes](https://mlsanity.com/paper/2404.02830/) (2024)
- [HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture Grading](https://mlsanity.com/paper/2503.05990/) (2025)
- [Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification](https://mlsanity.com/paper/2601.15235/) (2026)

## Abstract (arXiv)

Osteoporosis induced fractures occur worldwide about every 3 seconds. Vertebral compression fractures are early signs of the disease and considered risk predictors for secondary osteoporotic fractures. We present a detection method to opportunistically screen spine-containing CT images for the presence of these vertebral fractures. Inspired by radiology practice, existing methods are based on 2D and 2.5D features but we present, to the best of our knowledge, the first method for detecting vertebral fractures in CT using automatically learned 3D feature maps. The presented method explicitly localizes these fractures allowing radiologists to interpret its results. We train a voxel-classification 3D Convolutional Neural Network (CNN) with a training database of 90 cases that has been semi-automatically generated using radiologist readings that are readily available in clinical practice. Our 3D method produces an Area Under the Curve (AUC) of 95% for patient-level fracture detection and an AUC of 93% for vertebra-level fracture detection in a five-fold cross-validation experiment.