Statistical Shape Model with Random Walks for Inner Ear Segmentation

  • Esmeralda Ruiz Pujadas
  • Hans Martin Kjer
  • Gemma Piella
  • Miguel Angel González Ballester
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10126)


Cochlear implants can restore hearing to completely or partially deaf patients. The intervention planning can be aided by providing a patient-specific model of the inner ear. Such a model has to be built from high resolution images with accurate segmentations. Thus, a precise segmentation is required. We propose a new framework for segmentation of micro-CT cochlear images using random walks combined with a statistical shape model (SSM). The SSM allows us to constrain the less contrasted areas and ensures valid inner ear shape outputs. Additionally, a topology preservation method is proposed to avoid the leakage in the regions with no contrast.


Random walks Segmentation Shape prior Iterative segmentation Distance map prior Statistical shape model SSM Cochlea segmentation Inner ear segmentation 



The research leading to these results received funding from the European Union Seventh Frame Programme (FP7/2007–2013) under grant agreement 304857, HEAR-EU Project.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Esmeralda Ruiz Pujadas
    • 1
  • Hans Martin Kjer
    • 2
  • Gemma Piella
    • 1
  • Miguel Angel González Ballester
    • 1
    • 3
  1. 1.Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
  2. 2.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkLyngbyDenmark
  3. 3.ICREABarcelonaSpain

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