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Proceedings Paper

Automated anatomical labeling of MRI brain data using spatial atlas warping in a finite-element framework
Author(s): Dominik S. Meier; Elizabeth Fisher; Jean A. Tkach; Thomas J. Masaryk; Jeffrey A. Cohen; J. Fredrick Cornhill
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Paper Abstract

Identification of anatomical structures in magnetic resonance (MR) images of the human brain is achieved either by manual delineation of by applying coordinate system transformations to map the brain to a pre-labeled atlas. Manual segmentation of 3D MR data is a tedious task made additionally difficult by limitations in visualization. Affine transforms, like the Talairach stereotaxic space, perform a linear scaling of the brain based on manually selected landmarks. This often results in unsatisfactory accuracy for structures further away from the selected landmarks, particularly in pathological cases. It is also based on the trivializing assumption that the brain can be represented as a linearly scalable structure. In the effort to achieve a more accurate and consistent labeling, an algorithm has been designed for the automated alignment of a pre-labeled 3D brain atlas with a sample MRI volume. Alignment is achieved by elastically warping a finite element model of the atlas. The deformation is driven by a set of displacement constraints on the surface of individual brain structures. Solving this model results in a 3D displacement field for the entire atlas brain that aligns the segmented brain structure while extrapolating the deformation field to neighboring structures. The use of finite element modeling assures that this extrapolation occurs in a physically meaningful manner. The algorithm's performance was tested by matching the atlas image to warped versions of itself and to an individual sample brain. The amount of structural overlap achieved by a linear Talairach transform is also given for comparison. Elastic warping showed better performance compared to an affine transform alone or the Talairach method. Overlap increases with subsequent iterations with improvement directly related to the amount of model deformation.

Paper Details

Date Published: 24 June 1998
PDF: 12 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310851
Show Author Affiliations
Dominik S. Meier, Cleveland Clinic Foundation and The Ohio State Univ. (United States)
Elizabeth Fisher, Cleveland Clinic Foundation and The Ohio State Univ. (United States)
Jean A. Tkach, Cleveland Clinic Foundation and The Ohio State Univ. (United States)
Thomas J. Masaryk, Cleveland Clinic Foundation (United States)
Jeffrey A. Cohen, Cleveland Clinic Foundation (United States)
J. Fredrick Cornhill, Cleveland Clinic Foundation and The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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