Shape based segmentation of anatomical structures in magnetic resonance images

Authors:
Kilian M. Pohl;John Fisher;Ron Kikinis;W. Eric L. Grimson;William M. Wells
Affiliations:
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA;Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA;Surgical Planning Laboratory, Harvard Medical School, and Brigham and Women’s Hospital, Boston, MA;Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA;Surgical Planning Laboratory, Harvard Medical School, and Brigham and Women’s Hospital, Boston, MA
Venue:
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Year:
2005

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Abstract

Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We present an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior information. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. Structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the maximum a posteriori probability estimation problem. We demonstrate the approach on 20 brain magnetic resonance images showing superior performance, particularly in cases where purely image based methods fail.