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Estimating the confidence of statistical model based shape prediction

R. Blanc, E. Syrkina and G. Székely
Information Processing in Medical Imaging
July 2009

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 the 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.


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@InProceedings{eth_biwi_00662,
  author = {R. Blanc and E. Syrkina and G. Székely},
  title = {Estimating the confidence of statistical model based shape prediction},
  booktitle = {Information Processing in Medical Imaging},
  year = {2009},
  month = {July},
  pages = {602-613},
  volume = {5636},
  series = {LNCS},
  publisher = {Springer},
  keywords = {Statistical shape model, shape prediction, confidence regions}
}