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Automatic detection of vertebral number abnormalities in body CT images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The anatomical anomaly of the number of vertebral bones is one of the major anomalies in the human body, which can cause confusion of the spinal level in, for example, surgery. The aim of this study is to develop an automatic detection system for this type of anomaly.

Methods

We utilized our previously reported anatomical landmark detection system for this anomaly detection problem. This system uses a landmark point distribution model (L-PDM) to find multiple landmark positions. The L-PDM is a statistical probabilistic model of all landmark positions in the human body, including five landmarks for each vertebra. Given a new volume, the proposed algorithm applies five hypotheses (normal, 11 or 13 thoracic vertebrae, 4 or 6 lumbar vertebrae) to the given spine and attempts to detect all the landmarks. Then, the most plausible hypothesis with the largest posterior likelihood is selected as the anatomy detection result.

Results

The proposed method was evaluated using 300 neck-to-pelvis CT datasets. For normal subjects, the vertebrae of 211/217 (97.2%) of the subjects were successfully determined as normal. For subjects with 23 or 25 vertebrae without a transitional vertebra (TV), the vertebrae of 9/10 (90%) of the subjects were successfully determined. For subjects with TV, the vertebrae of 71/73 (97.3%) of subjects were judged as partially successfully determined.

Conclusion

Our algorithm successfully determined the number of vertebrae, and the feasibility of our proposed system was validated.

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Acknowledgements

This work was supported in part by JSPS Grants-in-Aid for Scientific Research KAKENHI Grant Numbers 15H01108 and 15K19775.

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Authors and Affiliations

  1. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Shouhei Hanaoka & Yoshiyasu Nakano

  2. Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan

    Shouhei Hanaoka & Akinobu Shimizu

  3. Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan

    Mitsutaka Nemoto, Yukihiro Nomura, Tomomi Takenaga, Soichiro Miki, Takeharu Yoshikawa & Naoto Hayashi

  4. Department of Intelligent Systems, Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan

    Yoshitaka Masutani

Authors
  1. Shouhei Hanaoka
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  2. Yoshiyasu Nakano
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  3. Mitsutaka Nemoto
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  4. Yukihiro Nomura
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  5. Tomomi Takenaga
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  7. Takeharu Yoshikawa
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  8. Naoto Hayashi
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  9. Yoshitaka Masutani
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  10. Akinobu Shimizu
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Corresponding author

Correspondence to Shouhei Hanaoka.

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The authors declare that they have no conflict of interest.

Ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki Declaration, as revised in 2008(5). For this type of study, formal consent is not required.

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Hanaoka, S., Nakano, Y., Nemoto, M. et al. Automatic detection of vertebral number abnormalities in body CT images. Int J CARS 12, 719–732 (2017). https://doi.org/10.1007/s11548-016-1516-y

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  • DOI: https://doi.org/10.1007/s11548-016-1516-y

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