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Optimizing Boundary Detection via Simulated Search with Applications to Multi-Modal Heart Segmentation
Medical Image Analysis, 10/27/09
Peters J et al. – The authros also illustrate how the Simulated Search can be used to identify suitable classes of features when addressing a new segmentation task. Finally, authors show that the Simulated Search enables multi–modal heart segmentation using a single algorithmic framework. On computed tomography and magnetic resonance images, average segmentation errors for the four chambers and the trunks of the large vessels are in the order of 0.8mm. For 3D rotational X–ray angiography images of the left atrium and pulmonary veins, the average error is 1.3mm. In all modalities, the locally optimized boundary detection enables fully automatic segmentation.
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