A Bayesian model for joint segmentation and registration

A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation.

Authors

• Kilian M. Pohl
• John Fisher
• W. Eric L. Grimson
• Ron Kikinis
• William M. Wells

Keywords

• Registration
• Segmentation
• Subcortical segmentation
• Bayesian modeling
• Expectation–Maximization