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Abstract: Assessment of Segmentation Dependence in Macroscopic Lung Cavity Extraction

  • Asmaa Khdeir
  • Tobias Geimer
  • Shuqing Chen
  • Eric Goppert
  • Maximilian Dankbar
  • Christoph Bert
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Training of respiratory motion models and population-based patient phantoms of the lung often requires the definition of the entire lung cavity region in the 4D-CT. To ease the workload of clinical experts, automatic selection is highly desirable. Many lung cavity extraction methods rely on a pre-segmented lung volume.

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Asmaa Khdeir
    • 1
    • 2
  • Tobias Geimer
    • 1
    • 2
    • 3
  • Shuqing Chen
    • 1
  • Eric Goppert
    • 1
  • Maximilian Dankbar
    • 1
  • Christoph Bert
    • 2
    • 3
  • Andreas Maier
    • 1
    • 3
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.Department of Radiation OncologyUniversitätsklinikum Erlangen, FAU Er-NErlangenDeutschland
  3. 3.Erlangen Graduate School in Advanced Optical TechnologiesFAU Er-NErlangenDeutschland

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