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Updated: Mar 6, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
Published on: November 14, 2019
Incorporating shape prior into active contours with a sparse linear combination of training shapes: application to
Abstract:
In this paper, a novel method of embedding shape information into level set image segmentation is proposed. Our method is based on inferring shape variations by a sparse linear combination of instances in the shape repository. Given a sufficient number of training shapes with variations, a new shape can be approximated by a linear span of training shapes associated with those variations. At each step of curve evolution the curve is moved to minimize Chan-Vese energy functional as well as toward the best approximation based on a linear combination of training samples. Although the method is general, in this paper it has been applied to the problem of segmentation of corpus callosum from 2D sagittal MR images.
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