Active mean fields: solving the mean field approximation in the level set framework

Authors:
Kilian M. Pohl;Ron Kikinis;William M. Wells
Affiliations:
Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA and Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, ...;Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA;Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA and Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, ...
Venue:
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Year:
2007

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Abstract

We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribution on labels is sought via the Mean Field approach. Optimizing the resulting estimator by gradient descent leads to a level set style algorithm where the level set functions are the logarithm-of-odds encoding of the posterior label probabilities in an unconstrained linear vector space. Applications with more than two labels are easily accommodated. The label assignment is accomplished by the Maximum A Posteriori rule, so there are no problems of "overlap" or "vacuum'. We test the method on synthetic images with additive noise. In addition, we segment a magnetic resonance scan into the major brain compartments and subcortical structures.