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• W1977772244 abstract "A stochastic deformable model is proposed for the segmentation of the myocardium in Magnetic Resonance Imaging. The segmentation is posed as a probabilistic optimization problem in which the optimal time-dependent surface is obtained for the myocardium of the heart in a discrete space of locations built upon simple geometric assumptions. For this purpose, first, the left ventricle is detected by a set of image analysis tools gathered from the literature. Then, the segmentation solution is obtained by the Maximization of the Posterior Marginals for the myocardium location in a Markov Random Field framework which optimally integrates temporal-spatial smoothness with intensity and gradient related features in an unsupervised way by the Maximum Likelihood estimation of the parameters of the field. This scheme provides a flexible and robust segmentation method which has been able to generate results comparable to manually segmented images for some derived cardiac function parameters in a set of 43 patients affected in different degrees by an Acute Myocardial Infarction." @default.
• W1977772244 created "2016-06-24" @default.
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• W1977772244 date "2011-06-01" @default.
• W1977772244 modified "2023-09-25" @default.
• W1977772244 title "Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model" @default.
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• W1977772244 doi "https://doi.org/10.1016/j.media.2011.01.002" @default.
• W1977772244 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21354361" @default.
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