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Propagating uncertainties in statistical model based shape prediction

Ekaterina Syrkina, Rémi Blanc, Gábor Székely
SPIE Medical Imaging
2011, in press

Abstract

This paper addresses the question of accuracy assessment and confidence regions estimation in statistical model based shape prediction. Shape prediction consists in estimating the shape of an organ based on a partial observation, due e.g. to a limited field of view or poorly contrasted images, and generally requires a statistical model. However, such predictions can be impaired by several sources of uncertainty, in particular the presence of noise in the observation, limited correlations between the predictors and the shape to predict, as well as limitations of the statistical shape model – in particular the number of training samples. We propose a framework which takes these into account and derives confidence regions around the predicted shape. Our method relies on the construction of two separate statistical shape models, for the predictors and for the unseen parts, and exploits the correlations between them assuming a joint Gaussian distribution. Limitations of the models are taken into account by jointly optimizing the prediction and minimizing the shape reconstruction error through cross-validation. An application to the prediction of the shape of the proximal part of the human tibia given the shape of the distal femur is proposed, as well as the evaluation of the reliability of the estimated confidence regions, using a database of 184 samples. Potential applications are reconstructive surgery, e.g. to assess whether an implant fits in a range of acceptable shapes, or functional neurosurgery when the target’s position is not directly visible and needs to be inferred from nearby visible structures


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@InProceedings{eth_biwi_00775,
  author = {Ekaterina Syrkina and Rémi Blanc and Gábor Székely},
  title = {Propagating uncertainties in statistical model based shape prediction},
  booktitle = {SPIE Medical Imaging},
  year = {2011},
  keywords = {Statistical shape models, shape prediction, confidence regions},
  note = {in press}
}