Decision Forests with Spatio-Temporal Features for Graph-Based Tumor Segmentation in 4D Lung CT

  • Hamidreza Mirzaei
  • Lisa Tang
  • Rene Werner
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)


We propose an automatic lung tumor segmentation in dynamic CT images that incorporates the novel use of tumor tissue deformations. In contrast to elastography imaging techniques for measuring tumor tissue properties, which require mechanical compression and thereby interrupt normal breathing, we completely avoid the use of any external physical forces. Instead, we calculate the tissue deformations during normal respiration using deformable registration. We investigate machine learning methods in order to discover the spatio-temporal dynamics that would help distinguish tumor from normal tissue deformation patterns and integrate this information into the segmentation process. Our method adapts an ensemble of decision trees combined with a 3D graph-based optimization that takes into account spatio-temporal consistency. The experimental results on patients with large tumors achieved an average F-measure accuracy of 0.79.


Lung tumor image segmentation registration CT images imbalance data machine learning graph-based optimization 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Hamidreza Mirzaei
    • 1
  • Lisa Tang
    • 2
  • Rene Werner
    • 3
  • Ghassan Hamarneh
    • 2
  1. 1.Computational Vision Lab.Simon Fraser UniversityCanada
  2. 2.Medical Image Analysis Lab.Simon Fraser UniversityCanada
  3. 3.Department of Computational NeuroscienceUniversity Medical Center Hamburg-EppendorfGermany

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