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Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data

  • Computer Application
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Abstract

Purpose

Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT).

Materials and methods

This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal.

Results

The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment.

Conclusion

The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans.

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Funding

Funding was provided by Asociación para la Investigación y el Desarrollo en Resonancia Magnética – ADIRM, Ministry of Economy, Industry and Competitiveness (Grant No. DPI2014-53401-C2-2-R).

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Authors and Affiliations

  1. QUIBIM SL, Valencia, Spain

    Ana Jimenez-Pastor, Angel Alberich-Bayarri, Belen Fos-Guarinos, Fabio Garcia-Castro, David Garcia-Juan & Luis Marti-Bonmati

  2. Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, Valencia, Spain

    Angel Alberich-Bayarri

  3. Computing Department, Imperial College London, London, UK

    Ben Glocker

  4. Radiology Department, La Fe Polytechnics and University Hospital, Valencia, Spain

    Luis Marti-Bonmati

Authors
  1. Ana Jimenez-Pastor
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  2. Angel Alberich-Bayarri
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  3. Belen Fos-Guarinos
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  4. Fabio Garcia-Castro
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  5. David Garcia-Juan
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  6. Ben Glocker
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  7. Luis Marti-Bonmati
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Correspondence to Ana Jimenez-Pastor.

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Jimenez-Pastor, A., Alberich-Bayarri, A., Fos-Guarinos, B. et al. Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data. Radiol med 125, 48–56 (2020). https://doi.org/10.1007/s11547-019-01079-9

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  • DOI: https://doi.org/10.1007/s11547-019-01079-9

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