Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach

Yuhua Gu, Virendra Kumar, Lawrence O. Hall, Dmitry B. Goldgof*, Ching-Yen Li, Rene Korn, Claus Bendtsen, Emmanuel Rios Velazquez, Andre Dekker, Hugo Aerts, Philippe Lambin, Xiuli Li, Jie Tian, Robert A. Gatenby, Robert J. Gillies

*Corresponding author for this work

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A single click ensemble segmentation (SCES) approach based on an existing "Click & Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76%, respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.
Original languageEnglish
Pages (from-to)692-702
JournalPattern Recognition
Issue number3
Publication statusPublished - Mar 2013


  • Image features
  • Delineation
  • Lung tumor
  • Lesion
  • CT
  • Region growing
  • Ensemble segmentation

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