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International Journal of Computer Assisted Radiology and Surgery

Published in:

01-05-2017 | Original Article

Automatic detection of vertebral number abnormalities in body CT images

Authors: Shouhei Hanaoka, Yoshiyasu Nakano, Mitsutaka Nemoto, Yukihiro Nomura, Tomomi Takenaga, Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, Yoshitaka Masutani, Akinobu Shimizu

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 5/2017

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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Title: Automatic detection of vertebral number abnormalities in body CT images
Authors: Shouhei Hanaoka
Yoshiyasu Nakano
Mitsutaka Nemoto
Yukihiro Nomura
Tomomi Takenaga
Soichiro Miki
Takeharu Yoshikawa
Naoto Hayashi
Yoshitaka Masutani
Akinobu Shimizu
Publication date: 01-05-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 5/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-016-1516-y

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Automatic detection of vertebral number abnormalities in body CT images

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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