Abstract
Accurate tumor lesion activity estimation is critical for tumor staging and follow up studies. Positron emission tomography (PET) successfully images and quantifies the lesion metabolic activity. Recently, PET images were modeled as a fuzzy Gaussian mixture to delineate tumor lesions accurately. Nonetheless, on the course of accurate delineation, chances are high to potentially end up with activity underestimation, due to the limited PET resolution, the reconstruction images suffer from partial volume effects (PVE). In this work, we propose a statistical lesion activity computation (SLAC) approach to robustly estimate the total lesion activity (TLA) directly from the modeled Gaussian partial volume mixtures. To evaluate the proposed method, synthetic lesions were simulated and reconstructed. TLA was estimated from 3 state-of-the-art PET delineation schemes for comparison. All schemes were evaluated with reference to the ground truth knowledge. The experimental results convey that the SLAC is robust enough for clinical use.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Weber, W.A., Figlin, R.: Monitoring cancer treatment with PET/CT: Does it make a difference? JNM 48, 36S–44S (2007)
Kelloff, G.J., Hoffman, J.M., Johnson, B., et al.: Progress and promise of FDG PET imaging for cancer patient management and oncologic drug development. Clin. Cancer Res. 11, 2785–2808 (2005)
Hatt, M., Visvikis, D., Albarghach, N., Tixier, F., Pradier, O., le Rest, C.C.: Prognostic value of 18F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology. EJNMMI 38, 1191–1202 (2011)
Wahl, R.L., Jacene, H., Kasamon, Y., Lodge, M.A.: From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. JNM 50, 122S–150S (2009)
Lucignani, G., Larson, S.M.: Doctor, What does my future hold? The prognostic values of FDG-PET in solid tumours. EJNMMI 37, 1032–1038 (2010)
Caillol, H., Hillion, A., Pieczynski, W.: Fuzzy Random Fields and Unsupervised Image Segmentation. IEEE TGRS 31, 801–810 (1993)
Caillol, H., Pieczynski, W., Hillion, A.: Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation. IEEE TIP 6, 425–440 (1997)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: A unifying framework for partial volume segmentation of brain MR images. IEEE TMI 22, 105–119 (2003)
Hatt, M., le Rest, C.C., Turzo, A., Roux, C., Visvikis, D.: A fuzzy locally adaptive bayesian segmentation approach for volume determination in PET. IEEE TMI 28, 881–893 (2009)
Hatt, M., le Rest, C.C., Descourt, P., Dekker, A., De Ruysscher, 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, 301–308 (2010)
Figueiredo, M.A.T., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE PAMI 24, 381–396 (2002)
Geets, X., Lee, J.A., Bol, A., Lonneux, M., Grégoire, V.: A gradient-based method for segmenting FDG-PET images: methodology and validation. EJNMMI 34, 1427–1438 (2007)
Van Dalen, J.A., Hoffmann, A.L., Dicken, V., Vogel, W.V., Wiering, B., Ruers, T.J., Karssemeijer, N., Oyen, W.J.G.: A novel iterative method for lesion delineation and volumetric quantification with FDG PET. Nucl. Med. Communications 28, 485–493 (2007)
Segars, W.P.: Development of a new dynamic NURBS-based cardiac-torso (NCAT) phantom. PhD Dissertation, The University of North Carolina (May 2001)
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, 46–52 (2004)
Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE TMI 13, 601–609 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
George, J. et al. (2011). 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) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-24319-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24318-9
Online ISBN: 978-3-642-24319-6
eBook Packages: Computer ScienceComputer Science (R0)