title: Integrated four dimensional registration and segmentation of dynamic renal MR images.
abstract: In this paper a novel approach for the registration and segmentation of dynamic contrast enhanced renal MR images is presented. This integrated method is motivated by the observation of the reciprocity between registration and segmentation in 4D time-series images. Fully automated Fourier-based registration with sub-voxel accuracy and semi-automated time-series segmentation were intertwined to improve the accuracy in a multi-step fashion. We have tested our algorithm on several real patient data sets. Clinical validation showed remarkable and consistent agreement between the proposed method and manual segmentation by experts.
authors.affiliation: Department of Biomedical Engineering, Columbia University, New York, NY, USA. ts2060@columbia.edu
authors.forename: Ting
authors.initials: T
authors.lastname: Song
authors.forename: Vivian S
authors.initials: VS
authors.lastname: Lee
authors.forename: Henry
authors.initials: H
authors.lastname: Rusinek
authors.forename: Samson
authors.initials: S
authors.lastname: Wong
authors.forename: Andrew F
authors.initials: AF
authors.lastname: Laine
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007668:Kidney / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D018511:Systems Integration