Skip to main content
Log in

PET functional volume delineation: a robustness and repeatability study

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Current state-of-the-art algorithms for functional uptake volume segmentation in PET imaging consist of threshold-based approaches, whose parameters often require specific optimization for a given scanner and associated reconstruction algorithms. Different advanced image segmentation approaches previously proposed and extensively validated, such as among others fuzzy C-means (FCM) clustering, or fuzzy locally adaptive bayesian (FLAB) algorithm have the potential to improve the robustness of functional uptake volume measurements. The objective of this study was to investigate robustness and repeatability with respect to various scanner models, reconstruction algorithms and acquisition conditions.

Methods and materials

Robustness was evaluated using a series of IEC phantom acquisitions carried out on different PET/CT scanners (Philips Gemini and Gemini Time-of-Flight, Siemens Biograph and GE Discovery LS) with their associated reconstruction algorithms (RAMLA, TF MLEM, OSEM). A range of acquisition parameters (contrast, duration) and reconstruction parameters (voxel size) were considered for each scanner model, and the repeatability of each method was evaluated on simulated and clinical tumours and compared to manual delineation.

Results

For all the scanner models, acquisition parameters and reconstruction algorithms considered, the FLAB algorithm demonstrated higher robustness in delineation of the spheres with low mean errors (10%) and variability (5%), with respect to threshold-based methodologies and FCM. The repeatability provided by all segmentation algorithms considered was very high with a negligible variability of <5% in comparison to that associated with manual delineation (5–35%).

Conclusion

The use of advanced image segmentation algorithms may not only allow high accuracy as previously demonstrated, but also provide a robust and repeatable tool to aid physicians as an initial guess in determining functional volumes in PET.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Lucignani G. SUV and segmentation: pressing challenges in tumor assessment and treatment. Eur J Nucl Med Mol Imaging. 2009;36:715–20.

    Article  PubMed  Google Scholar 

  2. Jarritt H, Carson K, Hounsel AR, Visvikis D. The role of PET/CT scanning in radiotherapy planning. Br J Radiol. 2006;79(S):27–35.

    Article  Google Scholar 

  3. Pan T, Mawlawi O. PET/CT in radiation oncology. Med Phys. 2008;35(11):4955–66.

    Article  PubMed  Google Scholar 

  4. Krak NC, Boellaard R, Hoekstra OS, Twisk JW, Hoekstra CJ, Lammertsma AA, et al. Effects of ROI definition and reconstruction method on quantitative outcome and applicability in a response monitoring trial. Eur J Nucl Med Mol Imaging. 2005;32:294–301.

    Article  PubMed  Google Scholar 

  5. Jerusalem G, Hustinx R, Beguin Y, Fillet G. The value of positron emission tomography (PET) imaging in disease staging and therapy assessment. Ann Oncol. 2002;13(S4):227–34.

    PubMed  Google Scholar 

  6. Erdi YE, Mawlawi O, Larson SM, Imbriaco M, Yeung H, Finn R, et al. Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer. 1997;80(12 Suppl):2505–9.

    Article  PubMed  CAS  Google Scholar 

  7. Daisne JF, Sibomana M, Bol A, Doumont T, Lonneux M, Grégoire V. Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms. Radiother Oncol. 2003;69:247–50.

    Article  PubMed  Google Scholar 

  8. Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rübe C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J Nucl Med. 2005;46(8):1342–8.

    PubMed  Google Scholar 

  9. Biehl KJ, Kong MF, Dehdashti F, Jin JY, Mutic S, El Naqa I, et al. 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: is a single standardized uptake value threshold approach appropriate? J Nucl Med. 2006;47:1808–12.

    PubMed  Google Scholar 

  10. Oellers M, Bosmans G, van Baardwijk A, Dekker A, Lambin P, Teule J, et al. The integration of PET-CT scans from different hospitals into radiotherapy treatment planning. Radiother Oncol. 2008;87(1):142–6.

    Article  Google Scholar 

  11. El Naqa I, Yang D, Apte A, Khullar D, Mutic S, Zheng J, et al. Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning. Med Phys. 2007;34(12):4738–49.

    Article  PubMed  Google Scholar 

  12. Montgomery DW, Amira A, Zaidi H. Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model. Med Phys. 2007;34(2):722–36.

    Article  PubMed  Google Scholar 

  13. Geets X, Lee JA, Bol A, Lonneux M, Grégoire V. A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging. 2007;34:1427–38.

    Article  PubMed  Google Scholar 

  14. Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28(6):881–93.

    Article  PubMed  Google Scholar 

  15. Hatt M, Cheze le Rest C, Descourt P, Dekker A, De Ruysscher D, Oellers M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010;77(1):301–8.

    Article  PubMed  Google Scholar 

  16. Dunn JC. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybernet. 1974;31:32–57.

    Google Scholar 

  17. Koh WJ, Rasey JS, Evans ML, Grierson JR, Lewellen TK, Graham MM, et al. Imaging of hypoxia in human tumors with [F-18]fluoromisonidazole. Int J Radiat Oncol Biol Phys. 1992;22(1):199–212.

    Article  PubMed  CAS  Google Scholar 

  18. Hatt M, Cheze Le Rest C, Aboagye EO, Kenny LM, Rosso L, Turkheimer FE, et al. Reproducibility of 18F-FDG and 3'-deoxy-3'-18F-fluorothymidine PET tumor volume measurements. J Nucl Med. 2010;51(9):1368–76.

    Article  PubMed  CAS  Google Scholar 

  19. Zhu W, Jiang T. Automation segmentation of PET image for brain tumors. IEEE Nucl Sci Symp Conf Rec. 2003;4:2627–9.

    Google Scholar 

  20. Belhassen S, Zaidi H. A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys. 2010;37(3):1309–24.

    Article  PubMed  Google Scholar 

  21. Celeux G, Diebolt J. L'algorithme SEM: un algorithme d'apprentissage probabiliste pour la reconnaissance de mélange de densités. Rev Statist Appl. 1986;34(2):35–52.

    Google Scholar 

  22. Le Maitre A, Segars WP, Marache S, Reilhac A, Hatt M, Tomei S, et al. Incorporating patient specific variability in the simulation of realistic whole body 18F-FDG distributions for oncology applications. Proc IEEE. 2009;97(12):2026–38.

    Article  Google Scholar 

Download references

Acknowledgments

This work was financially supported by the French National Research Agency (ANR) under contract ANR-08-ETEC-005-01. We would like to thank the following clinical centres and associated members for some of the phantom and patient datasets used in this study: the nuclear medicine departments of CHU Brest, France (Alexandre Turzo), CHU Sud-Amiens, France (Pascal Bailly, Joel Daouk), and St Bartholomew’s Hospital, London, UK (Iain Murray).

Conflicts of interest

None

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mathieu Hatt.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hatt, M., Cheze Le Rest, C., Albarghach, N. et al. PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging 38, 663–672 (2011). https://doi.org/10.1007/s00259-010-1688-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-010-1688-6

Keywords

Navigation