Cochlea Segmentation using Iterated Random Walks with Shape Prior

Esmeralda Ruiz Pujadas, Hans Martin Kjer, Sergio Vera, Mario Ceresa, Miguel Angel González Ballester

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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.
Original languageEnglish
Title of host publicationProceedings of SPIE
Number of pages9
PublisherSPIE - International Society for Optical Engineering
Publication date2016
Publication statusPublished - 2016
EventSPIE Medical Imaging 2016: Image Processing - San Diego, United States
Duration: 1 Mar 20163 Mar 2016
Conference number: 9784


ConferenceSPIE Medical Imaging 2016
Country/TerritoryUnited States
CitySan Diego
SeriesProceedings of SPIE - The International Society for Optical Engineering


  • Random walks
  • Shape prior
  • Distance map
  • Segmentation
  • Cochlea


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