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Automatic Detection of Wrist Fractures From Posteroanterior and Lateral Radiographs: A Deep Learning-Based Approach

  • Raja EbsimEmail author
  • Jawad Naqvi
  • Timothy F. Cootes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11404)

Abstract

We present a system that uses convolutional neural networks (CNNs) to detect wrist fractures (distal radius fractures) in posterioanterior and lateral radiographs. The proposed system uses random forest regression voting constrained local model to automatically segment the radius. The resulting automatic annotation is used to register the object across the dataset and crop patches. A CNN is trained on the registered patches for each view separately. Our automatic system outperformed existing systems with a performance of 96% (area under receiver operating characteristic curve) on cross-validation experiments on a dataset of 1010 patients, half of them with fractures.

Keywords

Medical image analysis with deep learning X-ray fracture detection Wrist fracture detection Computer-aided diagnosis 

Notes

Acknowledgements

The research leading to these results has received funding from Libyan Ministry of Higher Education and Research. The authors would like to thank Dr Jonathan Harris, Dr Matthew Davenport, and Dr Martin Smith for their help setting up the project.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Imaging SciencesThe University of ManchesterManchesterUK
  2. 2.Health Education North West School of RadiologyManchesterUK

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