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21 March 2016 Cochlea segmentation using iterated random walks with shape prior
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Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution µCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model. We propose a new framework for segmentation of µCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea. Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.
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Esmeralda Ruiz Pujadas, Hans Martin Kjer, Sergio Vera, Mario Ceresa, and Miguel Ángel González Ballester "Cochlea segmentation using iterated random walks with shape prior", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842U (21 March 2016);

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