  In this paper, we propose an efficient pseudo-marginal Markov chain Monte
Carlo (MCMC) sampling approach to draw samples from posterior shape
distributions for image segmentation. The computation time of the proposed
approach is independent from the size of the training set used to learn the
shape prior distribution nonparametrically. Therefore, it scales well for very
large data sets. Our approach is able to characterize the posterior probability
density in the space of shapes through its samples, and to return multiple
solutions, potentially from different modes of a multimodal probability
density, which would be encountered, e.g., in segmenting objects from multiple
shape classes. Experimental results demonstrate the potential of the proposed
approach.
