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.
Pohl
Kilian M
KM
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA. pohl@csail.mit.edu
Fisher
John
J
Grimson
W Eric L
WE
Kikinis
Ron
R
Wells
William M
WM
D016477:Artifacts; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000175:diagnosis*; D001931:Brain Mapping; D002552:Cerebral Ventricles / Q000473:pathology; D004292:Dominance, Cerebral / Q000502:physiology; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical; D013788:Thalamus / Q000473:pathology