title: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans.
abstract: This paper presents a new method for automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. No assumptions are made about which section of the spine is visible or to which extent. Thus, our approach is more general than previous work while being computationally efficient. Our algorithm is based on regression forests and probabilistic graphical models. The discriminative, regression part aims at roughly detecting the visible part of the spine. Accurate localization and identification of individual vertebrae is achieved through a generative model capturing spinal shape and appearance. The system is evaluated quantitatively on 200 CT scans, the largest dataset reported for this purpose. We obtain an overall median localization error of less than 6mm, with an identification rate of 81%.
authors.affiliation: Microsoft Research, Cambridge, UK.
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.forename: J
authors.initials: J
authors.lastname: Feulner
authors.forename: Antonio
authors.initials: A
authors.lastname: Criminisi
authors.forename: D R
authors.initials: DR
authors.lastname: Haynor
authors.forename: E
authors.initials: E
authors.lastname: Konukoglu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013131:Spine / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*