title: Robust registration of longitudinal spine CT.
abstract: Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration for clinical use, however, is challenging since standard registration techniques often fail due to poor initial alignment. The main causes of registration failure are the small overlap between scans which focus on different parts of the spine and/or substantial change in shape (e.g. after correction of abnormal curvature) and appearance (e.g. due to surgical implants). To overcome these issues we propose a registration approach which incorporates estimates of vertebrae locations obtained from a learning-based classification method. These location priors are used to initialize the registration and to provide semantic information within the optimization process. Quantitative evaluation on a database of 93 patients with a total of 276 registrations on longitudinal spine CT demonstrate that our registration method significantly reduces the number of failure cases.
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.forename: Darko
authors.initials: D
authors.lastname: Zikic
authors.forename: David R
authors.initials: DR
authors.lastname: Haynor
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; 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*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*