Improved inference in Bayesian segmentation using Monte Carlo sampling: Application to hippocampal subfield volumetry
An improved inference method for Bayesian segmentation using MCMC sampling. The sampling is used to approximate the integral over model parameters. We tested the method in a AD classification task using hippocampal subfield volumes. The method outperforms using point estimates of the parameters in the classification. The framework also provides informative error bars on the volume estimates.
segmentation monte carlo sampling hippocampal subfields bayesian modeling