Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 107–114Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Fuzzy Multi-class Statistical Modeling for Efficient Total Lesion Metabolic Activity Estimation from Realistic PET Images

Fuzzy Multi-class Statistical Modeling for Efficient Total Lesion Metabolic Activity Estimation from Realistic PET Images

  • Jose George19,20,21,
  • Kathleen Vunckx19,22,
  • Elke Van de Casteele19,20,21,
  • Sabine Tejpar23,
  • Christophe M. Deroose22,
  • Johan Nuyts19,22,
  • Dirk Loeckx19,21,24 &
  • …
  • Paul Suetens19,20,21 
  • Conference paper
  • 5437 Accesses

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) has become the de facto standard for current clinical therapy follow up evaluations. In pursuit of robust biomarkers for predicting early therapy response, an efficient marker quantification procedure is certainly a necessity. Among various PET derived markers, the clinical investigations indicated that the total lesion metabolic activity (TLA) of a tumor lesion has a good prognostic value in several longitudinal studies. We utilize a fuzzy multi-class modeling using a stochastic expectation maximization (SEM) algorithm to fit a finite mixture model (FMM) to the PET image. We then propose a direct estimation formula for TLA and SUVmean from this multi-class statistical model. In order to evaluate our proposition, a realistic liver lesion is simulated and reconstructed. All results were evaluated with reference to the ground truth knowledge. Our experimental study conveys that the proposed method is robust enough to handle background heterogeneities in realistic scenarios.

Keywords

  • 18F-FDG PET
  • SEM
  • FMM
  • TLA
  • fuzzy partial volume modeling
  • convex combination of random variables

Download conference paper PDF

References

  1. Caillol, H., Pieczynski, W., Hillion, A.: Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation. IEEE Trans. Image Process. 6(3), 425–440 (1997)

    CrossRef  Google Scholar 

  2. Celeux, G., Diebolt, J.: L’algorithme SEM: Un algorithme d’apprentissage probabiliste pour la reconnaissance de mélanges de densités. Revue de Statistique Appliquée 34(2), 35–52 (1986)

    MATH  Google Scholar 

  3. Costelloe, C.M., Macapinlac, H.A., Madewell, J.E., Fitzgerald, N.E., Mawlawi, O.R., Rohren, E.M., Raymond, A.K., Lewis, V.O., Anderson, P.M., Bassett Jr., R.L., Harrell, R.K., Marom, E.M.: 18 F-FDG PET/CT as an indicator of progression-free and overall survival in osteosarcoma. J. Nucl. Med. 50(3), 340–347 (2009)

    CrossRef  Google Scholar 

  4. Dempster, A.P., Laird, N.M., Jain, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B Stat. Meth. 39(1), 1–38 (1977)

    MATH  Google Scholar 

  5. Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 381–396 (2002)

    CrossRef  Google Scholar 

  6. George, J., Vunckx, K., Tejpar, S., Deroose, C.M., Nuyts, J., Loeckx, D., Suetens, P.: Fuzzy Statistical Unsupervised Learning Based Total Lesion Metabolic Activity Estimation in Positron Emission Tomography Images. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 233–240. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  7. Hatt, M., Le Rest, C.C., Aboagye, E.O., Kenny, L.M., Rosso, L., Turkheimer, F.E., Albarghach, N.M., Metges, J.P., Pradier, O., Visvikis, D.: Reproducibility of 18 F-FDG and 3’-Deoxy-3’-18 F-Fluorothymidine PET tumor volume measurements. J. Nucl. Med. 51(9), 1368–1376 (2010)

    CrossRef  Google Scholar 

  8. Hatt, M., Le Rest, C.C., Descourt, P., Dekker, A., Ruysscher, D.D., Oellers, M., Lambin, P., Pradier, O., Visvikis, D.: Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int. J. Radiation Oncology 77(1), 301–308 (2010)

    CrossRef  Google Scholar 

  9. Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans. Med. Imag. 13(4), 601–609 (1994)

    CrossRef  Google Scholar 

  10. Reilhac, A., Lartizien, C., Costes, N., Sans, S., Comtat, C., Gunn, R.N., Evans, A.C.: PET-SORTEO: A Monte Carlo-based simulator with high count rate capabilities. IEEE Trans. Nucl. Sci. 51(1), 46–52 (2004)

    CrossRef  Google Scholar 

  11. Segars, W.P.: Development of a new dynamic NURBS-based cardiac-torso (NCAT) phantom. PhD Dissertation, The University of North Carolina (2001)

    Google Scholar 

  12. Wahl, R.L., Jacene, H., Kasamon, Y., Lodge, M.A.: From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J. Nucl. Med. 50(suppl. 1), 122S–150S (2009)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Medical Imaging Research Center, UZ Leuven, Belgium

    Jose George, Kathleen Vunckx, Elke Van de Casteele, Johan Nuyts, Dirk Loeckx & Paul Suetens

  2. Future Health Department, IBBT-KU Leuven, Belgium

    Jose George, Elke Van de Casteele & Paul Suetens

  3. Medical Image Computing (ESAT/PSI/MIC), KU Leuven, Belgium

    Jose George, Elke Van de Casteele, Dirk Loeckx & Paul Suetens

  4. Nuclear Medicine, KU Leuven, Belgium

    Kathleen Vunckx, Christophe M. Deroose & Johan Nuyts

  5. Gastroenterology, KU Leuven, Belgium

    Sabine Tejpar

  6. icoMetrix NV, Leuven, Belgium

    Dirk Loeckx

Authors
  1. Jose George
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kathleen Vunckx
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elke Van de Casteele
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sabine Tejpar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christophe M. Deroose
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Johan Nuyts
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dirk Loeckx
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Paul Suetens
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Project Team Asclepios, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139,, Cambridge,, MA, USA

    Polina Golland

  3. Information and Communication, Nagoya University, 464-8603, Headquarters, Nagoya, Japan

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

George, J. et al. (2012). Fuzzy Multi-class Statistical Modeling for Efficient Total Lesion Metabolic Activity Estimation from Realistic PET Images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_14

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33415-3_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33414-6

  • Online ISBN: 978-3-642-33415-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature