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Multi-stage Learning for Robust Lung Segmentation in Challenging CT Volumes

  • Michal Sofka
  • Jens Wetzl
  • Neil Birkbeck
  • Jingdan Zhang
  • Timo Kohlberger
  • Jens Kaftan
  • Jérôme Declerck
  • S. Kevin Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Simple algorithms for segmenting healthy lung parenchyma in CT are unable to deal with high density tissue common in pulmonary diseases. To overcome this problem, we propose a multi-stage learning-based approach that combines anatomical information to predict an initialization of a statistical shape model of the lungs. The initialization first detects the carina of the trachea, and uses this to detect a set of automatically selected stable landmarks on regions near the lung (e.g., ribs, spine). These landmarks are used to align the shape model, which is then refined through boundary detection to obtain fine-grained segmentation. Robustness is obtained through hierarchical use of discriminative classifiers that are trained on a range of manually annotated data of diseased and healthy lungs. We demonstrate fast detection (35s per volume on average) and segmentation of 2 mm accuracy on challenging data.

Keywords

Interstitial Lung Disease Shape Model Statistical Shape Model Lung Surface Mesh Vertex 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michal Sofka
    • 1
  • Jens Wetzl
    • 1
  • Neil Birkbeck
    • 1
  • Jingdan Zhang
    • 1
  • Timo Kohlberger
    • 1
  • Jens Kaftan
    • 2
  • Jérôme Declerck
    • 2
  • S. Kevin Zhou
    • 1
  1. 1.Image Analytics and InformaticsSiemens Corporate ResearchPrincetonUSA
  2. 2.Molecular ImagingSiemens HealthcareOxfordUK

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