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Related Experiment Video

Updated: Apr 18, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

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Insight on automated lesion delineation methods for PET data.

Azadeh Firouzian1, Matthew D Kelly1, Jérôme M Declerck1

  • 1Siemens plc, Healthcare Sector, Molecular Imaging, 23/38 Hythe Bridge Street, Oxford OX1 2EP, UK.

EJNMMI Research
|January 17, 2015
PubMed
Summary

Contrast thresholding (CT) and adaptive thresholding (AT40) are recommended for accurate positron emission tomography (PET) tumor delineation. Incorporating background uptake and SUV harmonization filtering improves accuracy and consistency in treatment response assessment.

Keywords:
DelineationOncologyPETRadiotherapyReconstructionTumour volume

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Area of Science:

  • Medical Imaging
  • Radiotherapy Planning
  • Quantitative Imaging

Background:

  • Tumor volume definition for treatment response and radiotherapy planning is subjective and varies between observers.
  • Automated tumor delineation methods offer potential but have limitations.
  • Clinicians require practical guidance for selecting appropriate delineation methods.

Purpose of the Study:

  • To evaluate and compare the performance of six automated positron emission tomography (PET) delineation methods.
  • To assess the impact of various imaging parameters on delineation accuracy.
  • To provide data-driven recommendations for selecting PET delineation techniques.

Main Methods:

  • Six automated PET delineation methods were tested using NEMA IQ phantom and synthetic phantoms with diverse lesion characteristics.
  • Performance was evaluated using similarity index (SI) and percentage volume error (%VE).
  • The influence of contrast ratios, counts, realisations, reconstruction algorithms, and SUV harmonization (EQ.PET) was investigated.

Main Results:

  • Contrast thresholding (CT) demonstrated superior performance across various phantom types and settings (e.g., SI=0.83, %VE=5.65% for NEMA IQ spheres).
  • Adaptive thresholding at 40% (AT40) showed competitive results, especially without parameter tuning (e.g., SI=0.78, %VE=23.22%).
  • Applying EQ.PET improved AT40 performance for complex shapes and necrotic lesions (e.g., SI=0.61, %VE=14.83% for irregular shapes).

Conclusions:

  • Contrast thresholding (CT) and adaptive thresholding (AT40/AT50) are recommended for delineating lesions of varying sizes and contrasts.
  • Integrating background uptake information enhances PET delineation accuracy.
  • Employing SUV harmonization filtering (EQ.PET) prior to delineation improves accuracy and reduces variability across different imaging protocols.