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Abstract: Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data

  • Florin C. Ghesu
  • Bogdan Georgescu
  • Sasa Grbic
  • Andreas Maier
  • Joachim Hornegger
  • Dorin Comaniciu
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

An essential prerequisite for comprehensive medical image analysis is the robust and fast detection of anatomical structures in the human body. To this point, machine learning techniques are most often applied to address this problem, exploiting large annotated image databases to estimate parametric models for anatomy appearance. However, the performance of these methods is generally limited, due to suboptimal and exhaustive search strategies applied on large volumetric image data, e.g., 3D-CT scans.

Literatur

  1. 1.
    Ghesu FC, Georgescu B, Grbic S, et al. Robust multi-scale anatomical landmark detection in incomplete 3D-CT data. Proc MICCAI. 2017;Part I:194–202.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Florin C. Ghesu
    • 1
    • 2
  • Bogdan Georgescu
    • 1
  • Sasa Grbic
    • 1
  • Andreas Maier
    • 2
  • Joachim Hornegger
    • 2
  • Dorin Comaniciu
    • 1
  1. 1.Siemens HealthineersMedical Imaging TechnologiesPrincetonUSA
  2. 2.Pattern Recognition LabFriedrich-Alexander-Universität, ErlangenErlangenDeutschland

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