title: Estimating the confidence of statistical model based shape prediction.
abstract: We propose a method for estimating confidence regions around shapes predicted from partial observations, given a statistical shape model. Our method relies on the estimation of the distribution of the prediction error, obtained non-parametrically through a bootstrap resampling of a training set. It can thus be easily adapted to different shape prediction algorithms. Individual confidence regions for each landmark are then derived, assuming a Gaussian distribution. Merging those individual confidence regions, we establish th probability that, on average, a given proportion of the predicted landmarks actually lie in their estimated regions. We also propose a method for validating the accuracy of these regions using a test set.
authors.affiliation: Computer Vision Laboratory, ETHZ, Sternwartstrasse 7, 8092 Zürich, Switzerland. blanc@vision.ee.ethz.ch
authors.forename: Rémi
authors.initials: R
authors.lastname: Blanc
authors.forename: Ekaterina
authors.initials: E
authors.lastname: Syrkina
authors.forename: Gábor
authors.initials: G
authors.lastname: Székely
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016001:Confidence Intervals; D003337:Corpus Callosum / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity