Compression fractures detection on CT

Amir Bar, Lior Wolf, Orna Bergman Amitai, Eyal Toledano, Eldad Elnekave

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.

Original languageEnglish
Title of host publicationMedical Imaging 2017
Subtitle of host publicationComputer-Aided Diagnosis
EditorsNicholas A. Petrick, Samuel G. Armato
ISBN (Electronic)9781510607132
StatePublished - 2017
EventMedical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States
Duration: 13 Feb 201716 Feb 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE


ConferenceMedical Imaging 2017: Computer-Aided Diagnosis
Country/TerritoryUnited States


  • Compression fracture
  • Convolutional neural networks
  • Osteoporosis
  • Recurrent neural networks

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Compression fractures detection on CT'. Together they form a unique fingerprint.

Cite this