Automatic method for tumor segmentation from 3-points dynamic PET acquisitions

Francesco Verdoja, Marco Grangetto, Christian Bracco, Teresio Varetto, Manuela Racca, Michele Stasi

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


In this paper a novel technique to segment tumor voxels in dynamic positron emission tomography (PET) scans is proposed. An innovative anomaly detection tool tailored for 3-points dynamic PET scans is designed. The algorithm allows the identification of tumoral cells in dynamic FDG-PET scans thanks to their peculiar anaerobic metabolism experienced over time. The proposed tool is preliminarily tested on a small dataset showing promising performance as compared to the state of the art in terms of both accuracy and classification errors.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
Number of pages5
ISBN (Electronic)9781479957514
Publication statusPublished - 28 Jan 2014
MoE publication typeA4 Article in a conference publication


  • anomaly detection
  • image segmentation
  • Medical diagnostic imaging
  • positron emission tomography
  • tumors


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