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Machine Vision and Applications

, Volume 29, Issue 3, pp 405–414 | Cite as

Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-CT images

  • Esmeralda Ruiz Pujadas
  • Gemma Piella
  • Hans Martin Kjer
  • Miguel Angel González Ballester
Original Paper

Abstract

A cochlear implant is an electronic device which can restore sound to completely or partially deaf patients. For surgical planning, a patient-specific model of the inner ear must be built using high-resolution images accurately segmented. We propose a new framework for segmentation of micro-CT cochlear images using random walks, where a region term estimated by a Gaussian mixture model is combined with a shape prior initially obtained by a statistical shape model (SSM). The region term can then take advantage of the high contrast between the background and foreground, while the shape prior guides the segmentation to the exterior of the cochlea and to less contrasted regions inside the cochlea. The prior is obtained via a non-rigid registration regularized by a statistical shape model. The SSM constrains the inner parts of the cochlea and ensures valid output shapes of the inner ear.

Keywords

Inner ear segmentation for micro-CT images Statistical shape prior Statistical non-rigid registration Random walks segmentation 

Notes

Acknowledgements

The research leading to these results received funding from the European Union Seventh Frame Programme (FP7/2007-2013) under Grant agreement 304857.

Compliance with ethical standards

Conflict of interest

E.Ruiz, H.M Kjer, G.Piella and M.A González declare that they have no conflict of interest.

Human and animal rights

All human and animal studies have been approved.

Informed consent

All patients/volunteers gave their informed consent.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Esmeralda Ruiz Pujadas
    • 1
  • Gemma Piella
    • 1
  • Hans Martin Kjer
    • 2
  • 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 DenmarkKgs. LyngbyDenmark
  3. 3.ICREABarcelonaSpain

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