title: Automatic detection of vertebral number abnormalities in body CT images.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  Our algorithm successfully determined the number of vertebrae, and the feasibility of our proposed system was validated.
authors.affiliation: Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. hanaoka-tky@umin.ac.jp.|Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan. hanaoka-tky@umin.ac.jp.
authors.forename: Shouhei
authors.identifier: http://orcid.org/0000-0002-7496-1651
authors.initials: S
authors.lastname: Hanaoka
authors.affiliation: Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
authors.forename: Yoshiyasu
authors.initials: Y
authors.lastname: Nakano
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Mitsutaka
authors.initials: M
authors.lastname: Nemoto
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Yukihiro
authors.initials: Y
authors.lastname: Nomura
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Tomomi
authors.initials: T
authors.lastname: Takenaga
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Soichiro
authors.initials: S
authors.lastname: Miki
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Takeharu
authors.initials: T
authors.lastname: Yoshikawa
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Naoto
authors.initials: N
authors.lastname: Hayashi
authors.affiliation: Department of Intelligent Systems, Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan.
authors.forename: Yoshitaka
authors.initials: Y
authors.lastname: Masutani
authors.affiliation: Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
authors.forename: Akinobu
authors.initials: A
authors.lastname: Shimizu
mesh_terms: D000465:Algorithms; D006801:Humans; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging*; D008953:Models, Anatomic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011336:Probability; D011856:Radiographic Image Enhancement / Q000379:methods*; D013904:Thoracic Vertebrae / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Anatomical anomaly; Anatomical landmark; Computed tomography; Spine