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Data Descriptor: A multiscale imaging and modelling dataset of the human inner ear

  • Nicolas Gerber
  • , Mauricio Reyes
  • , Livia Barazzetti
  • , Hans Martin Kjer
  • , Sergio Vera
  • , Martin Stauber
  • , Pavel Mistrik
  • , Mario Ceresa
  • , Nerea Mangado
  • , Wilhelm Wimmer
  • , Thomas Stark
  • , Rasmus R. Paulsen
  • , Stefan Weber
  • , Marco Caversaccio
  • , Miguel A.González Ballester
  • University of Bern, Faculty of Medicine
  • University of Bern
  • Technical University of Denmark
  • Alma IT Systems
  • SCANCO Medical AG
  • Pompeu Fabra University (UPF)
  • Technical University of Munich
  • Inselspital Universitatsspital
  • Technology Department

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Understanding the human inner ear anatomy and its internal structures is paramount to advance hearing implant technology. While the emergence of imaging devices allowed researchers to improve understanding of intracochlear structures, the difficulties to collect appropriate data has resulted in studies conducted with few samples. To assist the cochlear research community, a large collection of human temporal bone images is being made available. This data descriptor, therefore, describes a rich set of image volumes acquired using cone beam computed tomography and micro-CT modalities, accompanied by manual delineations of the cochlea and sub-compartments, a statistical shape model encoding its anatomical variability, and data for electrode insertion and electrical simulations. This data makes an important asset for future studies in need of high-resolution data and related statistical data objects of the cochlea used to leverage scientific hypotheses. It is of relevance to anatomists, audiologists, computer scientists in the different domains of image analysis, computer simulations, imaging formation, and for biomedical engineers designing new strategies for cochlear implantations, electrode design, and others.

Original languageEnglish
Article number170132
JournalScientific Data
Volume4
Issue number1
DOIs
StatePublished - 19 Sep 2017

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