Dynamic Deformable Models for 3D MRI Heart Segmentation
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by Leonid Zhukov A, Zhaosheng Bao A, Igor Guskov B, John Wood C, David Breen A
http://www.stanford.edu/~jbao/SPIE.pdf
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Abstract:
Automated or semiautomated segmentation of medical images decreases interstudy variation, observer bias, and postprocessing time as well as providing clinically-relevant quantitative data. In this paper we present a new dynamic deformable modeling approach to 3D segmentation. It utilizes recently developed dynamic remeshing techniques and curvature estimation methods to produce high-quality meshes. The approach has been implemented in an interactive environment that allows a user to specify an initial model and identify key features in the data. These features act as hard constraints that the model must not pass through as it deforms. We have employed the method to perform semi-automatic segmentation of heart structures from cine MRI data.
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