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Suppose one is faced with the challenge of tissue segmentation in MR images,without annotators at their center to provide labeled training data. One optionis to go to another medical center for a trained classifier. Sadly, tissueclassifiers do not generalize well across centers due to voxel intensity shiftscaused by center-specific acquisition protocols. However, certain aspects ofsegmentations, such as spatial smoothness, remain relatively consistent and canbe learned separately. Here we present a smoothness prior that is fit tosegmentations produced at another medical center. This informative prior ispresented to an unsupervised Bayesian model. The model clusters the voxelintensities, such that it produces segmentations that are similarly smooth tothose of the other medical center. In addition, the unsupervised Bayesian modelis extended to a semi-supervised variant, which needs no visual interpretationof clusters into tissues.

A cross-center smoothness prior for variational Bayesian brain tissue segmentation