title: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets.
abstract: Expert landmark correspondences are widely reported for evaluating deformable image registration (DIR) spatial accuracy. In this report, we present a framework for objective evaluation of DIR spatial accuracy using large sets of expert-determined landmark point pairs. Large samples (>1100) of pulmonary landmark point pairs were manually generated for five cases. Estimates of inter- and intra-observer variation were determined from repeated registration. Comparative evaluation of DIR spatial accuracy was performed for two algorithms, a gradient-based optical flow algorithm and a landmark-based moving least-squares algorithm. The uncertainty of spatial error estimates was found to be inversely proportional to the square root of the number of landmark point pairs and directly proportional to the standard deviation of the spatial errors. Using the statistical properties of this data, we performed sample size calculations to estimate the average spatial accuracy of each algorithm with 95% confidence intervals within a 0.5 mm range. For the optical flow and moving least-squares algorithms, the required sample sizes were 1050 and 36, respectively. Comparative evaluation based on fewer than the required validation landmarks results in misrepresentation of the relative spatial accuracy. This study demonstrates that landmark pairs can be used to assess DIR spatial accuracy within a narrow uncertainty range.
authors.affiliation: Department of Imaging Physics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Castillo
authors.forename: Edward
authors.initials: E
authors.lastname: Castillo
authors.forename: Rudy
authors.initials: R
authors.lastname: Guerra
authors.forename: Valen E
authors.initials: VE
authors.lastname: Johnson
authors.forename: Travis
authors.initials: T
authors.lastname: McPhail
authors.forename: Amit K
authors.initials: AK
authors.lastname: Garg
authors.forename: Thomas
authors.initials: T
authors.lastname: Guerrero
mesh_terms: D000465:Algorithms; D004938:Esophageal Neoplasms / Q000000981:diagnostic imaging / Q000628:therapy; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards*; D013902:Radiography, Thoracic; D012015:Reference Standards; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed; D035501:Uncertainty