Statistical shape model with random walks for inner ear segmentation

Esmeralda Ruiz Pujadas, Hans Martin Kjer, Gemma Piella, Miguel Angel González Ballester

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationRevised Selected Papers of the 1st International Workshop on Spectral and Shape Analysis in Medical Imaging (SeSAMI 2016)
PublisherSpringer
Publication date2016
Pages92-102
ISBN (Print)978-3-319-51236-5
ISBN (Electronic)978-3-319-51237-2
DOIs
Publication statusPublished - 2016
Event1st International Workshop on Spectral and Shape Analysis in Medical Imaging (SeSAMI 2016) - Athens, Greece
Duration: 21 Oct 201621 Oct 2016
Conference number: 1
https://sites.google.com/site/sesami2016/

Workshop

Workshop1st International Workshop on Spectral and Shape Analysis in Medical Imaging (SeSAMI 2016)
Number1
CountryGreece
CityAthens
Period21/10/201621/10/2016
Internet address
SeriesLecture Notes in Computer Science
Volume10126
ISSN0302-9743

Keywords

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

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