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15 March 2006 Modeling shape variability for full heart segmentation in cardiac computed-tomography images
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An efficient way to improve the robustness of the segmentation of medical images with deformable models is to use a priori shape knowledge during the adaptation process. In this work, we investigate how the modeling of the shape variability in shape-constrained deformable models influences both the robustness and the accuracy of the segmentation of cardiac multi-slice CT images. Experiments are performed for a complex heart model, which comprises 7 anatomical parts, namely the four chambers, the myocardium, and trunks of the aorta and the pulmonary artery. In particular, we compare a common shape variability modeling technique based on principal component analysis (PCA) with a more simple approach, which consists of assigning an individual affine transformation to each anatomical subregion of the heart model. We conclude that the piecewise affine modeling leads to the smallest segmentation error, while simultaneously offering the largest flexibility without the need for training data covering the range of possible shape variability, as required by PCA.
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Olivier Ecabert, Jochen Peters, and Jürgen Weese "Modeling shape variability for full heart segmentation in cardiac computed-tomography images", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61443R (15 March 2006);

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