Abstract
Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.
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Wahl, R.L., et al.: From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors. Journal of Nuclear Medicine 50, 122S–150S (2009)
Grosu, A.L., et al.: Reirradiation of recurrent high-grade gliomas using amino acid PET (SPECT)/CT/MRI image fusion to determine gross tumor volume for stereotactic fractionated radiotherapy. International Journal of Radiation Oncology Biology Physics 63(2), 511–519 (2005)
Grady, L.: Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)
Stefano, A., Vitabile, S., Russo, G., Ippolito, M., Sardina, D., Sabini, M.G., Gallivanone, F., Castiglioni, I., Gilardi, M.C.: A graph-based method for PET image segmentation in radiotherapy planning: a pilot study. In: Petrosino, A. (ed.) ICIAP 2013, Part II. LNCS, vol. 8157, pp. 711–720. Springer, Heidelberg (2013)
Jentzen, W., et al.: Segmentation of PET volumes by iterative image thresholding. Journal of Nuclear Medicine 48(1), 108–114 (2007)
Li, H., et al.: A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours. Medical Physics 35(8), 3711–3721 (2008)
Geets, X., et al.: A gradient-based method for segmenting FDG-PET images: methodology and validation. European Journal of Nuclear Medicine and Molecular Imaging 34(9), 1427–1438 (2007)
Wanet, M., et al.: Gradient-based delineation of the primary GTV on FDG-PET in non-small cell lung cancer: A comparison with threshold-based approaches, CT and surgical specimens. Radiotherapy and Oncology 98(1), 117–125 (2011)
Hatt, M., et al.: A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET. IEEE Transactions on Medical Imaging 28(6), 881–893 (2009)
Zaidi, H., et al.: Fuzzy clustering-based segmented attenuation correction in whole-body PET imaging. Physics in Medicine and Biology 47(7), 1143–1160 (2002)
Bagci, U., et al.: A Graph-Theoretic Approach for Segmentation of PET Images. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2011, 8479–8482 (2011)
Onoma, D.P., et al.: 3D Random walk based segmentation for lung tumor delineation in PET imaging. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1260–1263 (2012)
Song, Q., et al.: Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information. IEEE Transactions on Medical Imaging 32(9), 1685–1697 (2013)
Xia, Y., et al.: Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information. Computerized Medical Imaging and Graphics 36(1), 47–53 (2012)
Zaidi, H., El Naqa, I.: PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. European Journal of Nuclear Medicine and Molecular Imaging 37(11), 2165–2187 (2010)
Paquet, N., et al.: Within-patient variability of F-18-FDG: Standardized uptake values in normal tissues. Journal of Nuclear Medicine 45(5), 784–788 (2004)
Udupa, J.K., et al.: A framework for evaluating image segmentation algorithms. Computerized Medical Imaging and Graphics 30(2), 75–87 (2006)
Young, H., et al.: Measurement of clinical and subclinical tumour response using F-18 -fluorodeoxyglucose and positron emission tomography: Review and 1999 EORTC recommendations. European Journal of Cancer 35(13), 1773–1782 (1999)
Soret, M., Bacharach, S.L., Buvat, I.: Partial-volume effect in PET tumor imaging. Journal of Nuclear Medicine 48(6), 932–945 (2007)
Stefano, A., et al.: Metabolic impact of partial volume correction of 18F FDG PET-CT oncological stucies on the assessment of tumor response to treatment. Quarterly Journal of Nuclear Medicine and Molecular Imaging 58(4), 413–423 (2014)
Gallivanone, F., et al.: PVE Correction in PET-CT Whole-Body Oncological Studies From PVE-Affected Images. IEEE Transactions on Nuclear Science 58(3), 736–747 (2011)
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Stefano, A. et al. (2015). An Automatic Method for Metabolic Evaluation of Gamma Knife Treatments. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9279. Springer, Cham. https://doi.org/10.1007/978-3-319-23231-7_52
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DOI: https://doi.org/10.1007/978-3-319-23231-7_52
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