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International Workshop on Deep Learning in Medical Image Analysis

International Workshop on Multimodal Learning for Clinical Decision Support

DLMIA 2017, ML-CDS 2017: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support pp 74–82Cite as

Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10553))

Abstract

The cervical spine is a flexible anatomy and vulnerable to injury, which may go unnoticed during a radiological exam. Towards building an automatic injury detection system, we propose a localization framework for the cervical spine in X-ray images. The proposed framework employs a segmentation approach to solve the localization problem. As the cervical spine is a single connected component, we introduce a novel region-aware loss function for training a deep segmentation network that penalises disjoint predictions. Using data augmentation, the framework has been trained on a dataset of 124 images and tested on another 124 images, all collected from real life medical emergency rooms. The results show a significant improvement in performance over the previous state-of-the-art cervical vertebrae localization framework.

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References

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Author information

Authors and Affiliations

  1. City, University of London, London, England

    S. M. Masudur Rahman Al Arif & Greg Slabaugh

  2. University of Exeter, Exeter, England

    Karen Knapp

Authors
  1. S. M. Masudur Rahman Al Arif
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  2. Karen Knapp
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  3. Greg Slabaugh
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Corresponding author

Correspondence to S. M. Masudur Rahman Al Arif .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. IBM Research - Almaden, San Jose, California, USA

    Tanveer Syeda-Mahmood

  5. Universidade do Porto, Porto, Portugal

    João Manuel R.S. Tavares

  6. IBM Research - Almaden, San Jose, USA

    Mehdi Moradi

  7. University of Queensland, Brisbane, Queensland, Australia

    Andrew Bradley

  8. Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  9. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  10. Case Western Reserve University, Cleveland, Ohio, USA

    Anant Madabhushi

  11. Instituto Superior Técnico, Lisboa, Portugal

    Jacinto C. Nascimento

  12. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  13. University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  14. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

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Cite this paper

Al Arif, S.M.M.R., Knapp, K., Slabaugh, G. (2017). Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-67558-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67557-2

  • Online ISBN: 978-3-319-67558-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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