Automated tumour delineation using joint PET/CT information

Vaclav Potesil, Xiaolei Huang, Xiang Sean Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations


In this paper, we propose a new method for automated delineation of tumor boundaries in whole-body PET/CT by jointly using information from both PET and diagnostic CT images. Our method takes advantage of initial robust hot spot detection and segmentation performed in PET to provide a conservative tumor structure delineation. Using this estimate as initialization, a model for tumor appearance and shape in corresponding CT structures is learned and the model provides the basis for classifying each voxel to either lesion or background class. This CT classification is then probabilistically integrated with PET classification using the joint likelihood ratio test technique to derive the final delineation. More accurate and reproducible tumor delineation is achieved as a result of such multi-modal tumor delineation, without additional user intervention. The method is particular useful to improve the PET delineation result when there are clear contrast edges in CT between tumor and healthy tissue, and to enable CT segmentation guided by PET when such contrast difference is absent in CT.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2007
Subtitle of host publicationComputer-Aided Diagnosis
EditionPART 2
StatePublished - 2007
EventMedical Imaging 2007: Computer-Aided Diagnosis - San Diego, CA, United States
Duration: Feb 20 2007Feb 22 2007

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
NumberPART 2
ISSN (Print)1605-7422


ConferenceMedical Imaging 2007: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego, CA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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