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

Source: [Detection of vertebral fractures in CT using 3D Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa669d1e209131cea116d7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:54:21.040Z

Authors: Joeri Nicolaes, Steven Raeymaeckers, David Robben, Guido Wilms, Dirk Vandermeulen, Cesar Libanati, Marc Debois

Published: 2019-11-05T14:43:00.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Vertebra Level Fracture Recognition on Private Dataset](<https://www.sota2.com/research/sota/vertebra-level-fracture-recognition-on-private-dataset.md>): Vertebra Level Fracture Recognition; Private Dataset; ROC-AUC: 0.93.

- [Patient Level Fracture Recognition on Private Dataset](<https://www.sota2.com/research/sota/patient-level-fracture-recognition-on-private-dataset.md>): Patient Level Fracture Recognition; Private Dataset; Accuracy: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.01816>)

[PDF](<https://arxiv.org/pdf/1911.01816.pdf>)