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Lung Segmentation from CT with Severe Pathologies Using Anatomical Constraints

  • Neil Birkbeck
  • Timo Kohlberger
  • Jingdan Zhang
  • Michal Sofka
  • Jens Kaftan
  • Dorin Comaniciu
  • S. Kevin Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

The diversity in appearance of diseased lung tissue makes automatic segmentation of lungs from CT with severe pathologies challenging. To overcome this challenge, we rely on contextual constraints from neighboring anatomies to detect and segment lung tissue across a variety of pathologies. We propose an algorithm that combines statistical learning with these anatomical constraints to seek a segmentation of the lung consistent with adjacent structures, such as the heart, liver, spleen, and ribs. We demonstrate that our algorithm reduces the number of failed detections and increases the accuracy of the segmentation on unseen test cases with severe pathologies.

Keywords

Pathological Lung Healthy Lung Severe Pathology Statistical Shape Model Lung Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Neil Birkbeck
    • 1
  • Timo Kohlberger
    • 1
  • Jingdan Zhang
    • 1
  • Michal Sofka
    • 1
  • Jens Kaftan
    • 2
  • Dorin Comaniciu
    • 1
  • S. Kevin Zhou
    • 1
  1. 1.Imaging & Computer VisionSiemens Corporate TechnologyPrincetonUSA
  2. 2.Molecular ImagingSiemens HealthcareOxfordUK

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