Automatic detection of vertebral number abnormalities in body CT images
- 347 Downloads
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.
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.
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.
Our algorithm successfully determined the number of vertebrae, and the feasibility of our proposed system was validated.
KeywordsAnatomical anomaly Anatomical landmark Spine Computed tomography
This work was supported in part by JSPS Grants-in-Aid for Scientific Research KAKENHI Grant Numbers 15H01108 and 15K19775.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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.
- 2.Letourneau D, Kaus M, Wong R, Vloet A, Fitzpatrick DA, Gospodarowicz M, Jaffray DA (2008) Semiautomatic vertebrae visualization, detection, and identification for online palliative radiotherapy of bone metastases of the spine. Med Phys 35(1):367–376. doi: 10.1118/1.2820631 CrossRefPubMedGoogle Scholar
- 5.Glocker B, Zikic D, Konukoglu E, Haynor DR, Criminisi A (2013) Vertebrae localization in pathological spine CT via dense classification from sparse annotations. Int Conf Med Image Comput Comput Assist Interv (MICCAI) 16(Pt 2):262–270Google Scholar
- 15.Yao J, O’Connor SD, Summers RM (2006) Automated spinal column extraction and partitioning. In: 3rd IEEE international symposium on biomedical imaging: nano to macro, 2006, IEEE, pp 390–393Google Scholar
- 16.Ma J, Lu L, Zhan Y, Zhou X, Salganicoff M, Krishnan A (2010) Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Int Conf Med Image Comput Comput Assist Interv (MICCAI) 13(Pt 1):19–27Google Scholar
- 17.Glocker B, Feulner J, Criminisi A, Haynor DR, Konukoglu E (2012) Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. Int Conf Med Image Comput Comput Assist Interv (MICCAI) 15(Pt 3):590–598Google Scholar
- 21.Daenzer S, Freitag S, von Sachsen S, Steinke H, Groll M, Meixensberger J, Leimert M (2014) VolHOG: a volumetric object recognition approach based on bivariate histograms of oriented gradients for vertebra detection in cervical spine MRI. Med Phys 41(8):082305. doi: 10.1118/1.4890587 CrossRefPubMedGoogle Scholar
- 23.Hanaoka S, Shimizu A, Nemoto M, Nomura Y, Miki S, Yoshikawa T, Hayashi N, Ohtomo K, Masutani Y (2016) Automatic detection of over 100 anatomical landmarks in medical CT images: A framework with independent detectors and combinatorial optimization. Med Image Anal 35:192–214. doi: 10.1016/j.media.2016.04.001 CrossRefPubMedGoogle Scholar
- 24.Hanaoka S, Masutani Y, Nemoto M, Nomura Y, Miki S, Yoshikawa T, Hayashi N, Ohtomo K (2013) A multiple anatomical landmark detection system for body CT images. In: 2013 First international symposium on computing and networking, 4–6 December, pp 308-311. doi: 10.1109/CANDAR.2013.54
- 25.Murata N, Takenouchi T, Kanamori T, Eguchi S (2004) Information geometry of U-boost and bregman divergence. Neural Comput 16(7):1437–1481. doi: 10.1162/089976604323057452
- 26.Nemoto M, Masutani Y, Hanaoka S, Nomura Y, Yoshikawa T, Hayashi N, Yoshioka N, Ohtomo K (2011) A unified framework for concurrent detection of anatomical landmarks for medical image understanding. SPIE Med Imaging. doi: 10.1117/12.878327
- 27.Tu Z, Zhou XS, Bogoni L, Barbu A, Comaniciu D (2006) Probabilistic 3D polyp detection in CT images: the role of sample alignment. In: Computer vision and pattern recognition, 2006 IEEE computer society conference on IEEE, pp 1544–1551Google Scholar
- 31.Hanaoka S, Masutani Y, Nemoto M, Nomura Y, Yoshikawa T, Hayashi N, Yoshioka N, Ohtomo K (2011) Probabilistic modeling of landmark distances and structure for anomaly-proof landmark detection. Proc Third Int Workshop Math Found Comput Anat 2011:159–169Google Scholar
- 33.Sawada Y, Hontani H (2012) A study on graphical model structure for representing statistical shape model of point distribution model. In: Ayache N, Delingette H, Golland P, Mori K (eds) Medical image computing and computer-assisted intervention—MICCAI 2012: 15th International conference, Nice, France, October 1–5, 2012, Proceedings, Part II. Springer, Berlin, pp 470–477. doi: 10.1007/978-3-642-33418-4_58