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20 March 2015 Shape-based multi-region segmentation framework: application to 3D infants MRI data
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This paper presents a novel shape-guided multi-region variational region growing framework for extract- ing simultaneously thoracic and abdominal organs on 3D infants whole body MRI. Due to the inherent low quality of these data, classical segmentation methods tend to fail at the multi-segmentation task. To compensate for the low resolution and the lack of contrast and to enable the simultaneous segmentation of multiple organs, we introduce a segmentation framework on a graph of supervoxels that combines supervoxels intensity distribution weighted by gradient vector ow value and a shape prior per tissue. The intensity-based homogeneity criteria and the shape prior, encoded using Legendre moments, are added as energy terms in the functional to be optimized. The intensity-based energy is computed using both local (voxel value) and global (neighboring regions mean values, adjacent voxels values and distance to the neighboring regions) criteria. Inter-region con ict resolution is handled using a weighted Voronoi decomposition method, the weights being determined using tissues densities. The energy terms of the global energy equation are weighted using an information on growth direction and on gradient vector flow value. This allows us to either guide the segmentation toward the image natural edges if it is consistent with image and shape prior terms, or enforce the shape prior term otherwise. Results on 3D infants MRI data are presented and compared to a set of manual segmentations. Both visual comparison and quantitative measurements show good results.
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Sonia Dahdouh and Isabelle Bloch "Shape-based multi-region segmentation framework: application to 3D infants MRI data", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941312 (20 March 2015);

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