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The authors propose a novel approach for improving level set segmentation methods by embedding the potential functions from a discriminatively trained Conditional Random Field (CRF) into a level set energy function. The CRF terms can be efficiently estimated and lead to both discriminative local potentials and edge regularizes that take into account interactions among the labels. Unlike discrete CRFs, the use of a continuous level set framework allows the natural use of flexible continuous regularizes such as shape priors. They show promising experimental results for the method on two difficult medical image segmentation tasks.
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