Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach

  • Aliaksei Kerhet
  • Cormac Small
  • Harvey Quon
  • Terence Riauka
  • Russell Greiner
  • Alexander McEwan
  • Wilson Roa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)


Lung cancer represents the most deadly type of malignancy. In this work we propose a machine learning approach to segmenting lung tumours in Positron Emission Tomography (PET) scans in order to provide a radiation therapist with a “second reader” opinion about the tumour location. For each PET slice, our system extracts a set of attributes, passes them to a trained Support Vector Machine (SVM), and returns the optimal threshold value for distinguishing tumour from healthy voxels in that particular slice. We use this technique to analyse four different PET/CT 3D studies. The system produced fairly accurate segmentation, with Jaccard and Dice’s similarity coefficients between 0.82 and 0.98 (the areas outlined by the returned thresholds vs. the ones outlined by the reference thresholds). Besides the high level of geometric similarity, a significant correlation between the returned and the reference thresholds also indicates that during the training phase, the learning algorithm effectively acquired the dependency between the extracted attributes and optimal thresholds.


Support Vector Machine (SVM) Positron Emission Tomography (PET) Radiation Treatment Lung Cancer Gross Tumour Volume (GTV) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Aliaksei Kerhet
    • 1
  • Cormac Small
    • 2
  • Harvey Quon
    • 2
  • Terence Riauka
    • 3
  • Russell Greiner
    • 4
  • Alexander McEwan
    • 1
  • Wilson Roa
    • 2
  1. 1.Department of OncologyUniversity of AlbertaEdmontonCanada
  2. 2.Department of Radiation OncologyCross Cancer InstituteEdmontonCanada
  3. 3.Department of Medical PhysicsCross Cancer InstituteEdmontonCanada
  4. 4.Alberta Ingenuity Centre for Machine Learning, Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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