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Robust Cochlear Modiolar Axis Detection in CT

  • Wilhelm WimmerEmail author
  • Clair Vandersteen
  • Nicolas Guevara
  • Marco Caversaccio
  • Hervé Delingette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

The cochlea, the auditory part of the inner ear, is a spiral-shaped organ with large morphological variability. An individualized assessment of its shape is essential for clinical applications related to tonotopy and cochlear implantation. To unambiguously reference morphological parameters, reliable recognition of the cochlear modiolar axis in computed tomography (CT) images is required. The conventional method introduces measurement uncertainties, as it is based on manually selected and difficult to identify landmarks. Herein, we present an algorithm for robust modiolar axis detection in clinical CT images. We define the modiolar axis as the rotation component of the kinematic spiral motion inherent in the cochlear shape. For surface fitting, we use a compact shape representation in a 7-dimensional kinematic parameter space based on extended Plücker coordinates. It is the first time such a kinematic representation is used for shape analysis in medical images. Robust surface fitting is achieved with an adapted approximate maximum likelihood method assuming a Student-t distribution, enabling axis detection even in partially available surface data. We verify the algorithm performance on a synthetic data set with cochlear surface subsets. In addition, we perform an experimental study with four experts in 23 human cochlea CT data sets to compare the automated detection with the manually found axes. Axes found from co-registered high resolution \(\upmu \)CT scans are used for reference. Our experiments show that the algorithm reduces the alignment error providing more reliable modiolar axis detection for clinical and research applications.

Keywords

Kinematic surface recognition Approximate maximum likelihood Natural growth 

Notes

Acknowledgments

Supported by the Swiss National Science Foundation (no. P400P2_180822) and the French government (UCA\(^{\mathrm {JEDI}}\) - ANR-15-IDEX-01).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Université Côte d’Azur, Inria, EpioneSophia AntipolisFrance
  2. 2.Department of Otolaryngology, InselspitalUniversity of BernBernSwitzerland
  3. 3.Hearing Research Laboratory, ARTORG CenterUniversity of BernBernSwitzerland
  4. 4.Université Côte d’Azur, Centre Hospitalier Universitaire de Nice, Institut Universitaire de la Face et du CouNiceFrance

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