title: DIRBoost-an algorithm for boosting deformable image registration: application to lung CT intra-subject registration.
abstract: We introduce a boosting algorithm to improve on existing methods for deformable image registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well known in the field of machine learning. DIRBoost utilizes a method for automatic registration error detection to obtain estimates of local registration quality. All areas detected as erroneously registered are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We validated the DIRBoost algorithm on three different DIR methods (ANTS gSyn, NiftyReg, and DROP) on three independent reference datasets of pulmonary image scan pairs. DIRBoost reduced registration errors significantly and consistently on all reference datasets for each DIR algorithm, yielding an improvement of the registration accuracy by 5-34% depending on the dataset and the registration algorithm employed.
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, Room Q0S.459, 3584 CX Utrecht, The Netherlands. Electronic address: sascha@isi.uu.nl.
authors.forename: Sascha E A
authors.initials: SE
authors.lastname: Muenzing
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, Room Q0S.459, 3584 CX Utrecht, The Netherlands.
authors.forename: Max A
authors.initials: MA
authors.lastname: Viergever
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, Room Q0S.459, 3584 CX Utrecht, The Netherlands.
authors.forename: Josien P W
authors.initials: JP
authors.lastname: Pluim
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D008297:Male; 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; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Boosting; Deformable image registration; Machine learning; Pattern recognition