Estimating the Confidence of Statistical Model Based Shape Prediction

Authors:
Rémi Blanc;Ekaterina Syrkina;Gábor Székely
Affiliations:
Computer Vision Laboratory, ETHZ, Zürich, Switzerland 8092;Computer Vision Laboratory, ETHZ, Zürich, Switzerland 8092;Computer Vision Laboratory, ETHZ, Zürich, Switzerland 8092
Venue:
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Year:
2009

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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.