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Proceedings Paper

Cochlea segmentation using iterated random walks with shape prior
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Paper Abstract

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.

Paper Details

Date Published: 21 March 2016
PDF: 9 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842U (21 March 2016); doi: 10.1117/12.2208675
Show Author Affiliations
Esmeralda Ruiz Pujadas, Univ. Pompeu Fabra (Spain)
Hans Martin Kjer, Technical Univ. of Denmark (Denmark)
Sergio Vera, Alma Medical Imaging (Spain)
Mario Ceresa, Univ. Pompeu Fabra (Spain)
Miguel Ángel González Ballester, Univ. Pompeu Fabra (Spain)
ICREA Barcelona (Spain)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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