Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Segmentation is one of the crucial steps in applications of medical diagnosis. The accurate image segmentation method plays an important role in proper detection of disease, staging, diagnosis, radiotherapy treatment planning and monitoring. In the advances of image segmentation techniques, joint segmentation of PET-CT images has increasingly received much attention in the field of both clinic and image processing. PET - CT images have become a standard method for tumor delineation and cancer assessment. Due to low spatial resolution in PET and low contrast in CT images, automated segmentation of tumor in PET - CT images is a well-known puzzle task. This paper attempted to describe and review four innovative methods used in the joint segmentation of functional and anatomical PET - CT images for tumor delineation. For the basic knowledge, the state of the art image segmentation methods were briefly reviewed and fundamental of PET and CT images were briefly explained. Further, the specific characteristics and limitations of four joint segmentation methods were critically discussed.

     

     


  • Keywords


    Joint Segmentation; Tumor; PET and CT images; Review.

  • References


      [1] Aristophanous M, Penney BC, (2007), Martel MK, Pelizzari CA, “A gaussian mixture model for definition of lung tumor volumes in positron emission tomography”, Med Phys, Vol.34, pp. 4223–4235.

      [2] Ashamalla H, Rafla S, Parikh K, Mokhtar B, Goswami G, Kambam S, Abdel HD, Ross AP, Evola A, (2005), “The contribution of integrated PET/CT to the evolving definition of treatment volumes in radiation treatment planning in lung cancer”, International Journal of Radiation, Oncology, Biology and Physics, Vol.63(4), pp.1016–1023.

      [3] Aubert G, (2006), Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, 2nd ed., Springer, New York,

      [4] Aujol J and Aubert G, (2002), “Signed distance functions and viscosity solutions of discontinuous Hamilton–Jacobi equations”, Technical Report No.4507, (unpublished)

      [5] Baardwijk AV, Dooms C, Suylen RJV, et al, (2007), “The maximum uptake of (18)F-deoxyglucose on positron emission tomography scan correlates with survival, hypoxia inducible factor-1alpha and GLUT-1 in non-small cell lung cancer”, Eur J Cancer, Vol.43, pp. 1392–1398.

      [6] Bagci U, Udupa JK, Mendhiratta N, Foster B, Xu Z, Yao J, Chen X and Mollura DJ, (2013), “Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images”, Medical Image Analysis, Vol.17, pp.929–945.

      [7] Bagci U, Udupa JK, Yao J, Mollura DJ, (2012), “Co-segmentation of functional and anatomical images”, Proceeding of Medical Image Computing and Computer-Assisted Intervention–MICCAI, Springer, pp: 459–467.

      [8] Bagci U, Yao J, Caban J, Turkbey E, Aras O, Mollura D, (2011), “A graph-theoretic approach for segmentation of pet images”, Engineering in Medicine and Biology Society, EMBS, Annual International Conference of the IEEE, pp. 8479–8482.

      [9] Ballangan C, Wang X, Fulham M, Eberl S, Feng D, (2013), “Lung tumor segmentation in PET images using graph cuts,” Computer Methods and Programs in Biomedicine, Vol. 109, pp. 260–268.

      [10] Basu S, Kwee T, Surti S, Akin E, Yoo D, Alavi A, (2011), “Fundamentals of pet and pet/ct imaging”, Annals of the New York Academy of Sciences, Vol.1228, pp.1–18.

      [11] Belhassen S and Zaidi H, (2010), “A novel fuzzy c-means algorithm for unsupervised heterogeneous tumor quantification in PET”, Medical Physics, Vol. 37(3), pp. 1309–1324.

      [12] Biehl K, Kong F, Dehdashti F, Jin J, Mutic S, ElNaqa I, Siegel B, Bradley J, (2006), “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, Vol. 47 (11), pp. 1808–1812.

      [13] Boellaard R. Krak N, Hoekstra O, Lammertsma A, (2004), “Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study”, Journal of Nuclear Medicine, Vol.45, pp.1519–1527.

      [14] Boudraa A, Zaidi H, (2006), Image segmentation techniques in nuclear medicine imaging, in: Zaidi (Ed), Quantitative analysis ofnuclear medicine imaging, Springer, New York, pp:308–357.

      [15] Boykov Y, Funka-Lea G, (2006), “Graph cuts and efficient and image segmentation”, International Journal of Computer Vision, Vol.70, pp.109–131.

      [16] Bradley JD, Perez CA, Dehdashti F, and Siegel BA, (2004), “Implementing biologic target volumes in radiation treatment planning for non-small cell lung cancer”, J Nucl Med, Vol.45, pp.96S–101S.

      [17] Brambilla M, Matheoud R, Secco C, Loi G, Krengli M, Inglese E, (2008), “Threshold segmentation for PET target volume delineation in radiation treatment planning: the role of target-to-background ratio and target size”, Med Phys, Vol. 35 (4), pp.1207–1213.

      [18] Chan TF, Sandberg BY, and Vese LA, (2000), “Active contours without edges for vector-valued images”, J. Vis. Commun. Image Represent, Vol. 11, p.130–141.

      [19] Ciernik I, Huser M, Burger C, Davis J, Szekely G, (2005), “Automated functional image-guided radiation treatment planning for rectal cancer”, International Journal of Radiation Oncology, Biology, Physics, Vol 62, pp.893–900.

      [20] Daisne J, Sibomana M, Bol A, Doumont T, Lonneux M, Grégoire V, (2003), “Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms”, Radiother Oncol, Vol.69(3), pp.247–250.

      [21] Davis J, Reiner B, Huser M, Burger C, Szekely G, Ciernik I, (2006), “Assessment of 18f PET signals for automatic target volume definition in radiotherapy treatment planning”, Radiother Oncol, Vol. 80, p. 43–50.

      [22] Day E, Betler J, Parda D, Reitz B, Kirichenko A, Mohammadi S, Miften M, (2009), “A region growing method for tumor volume segmentation on pet images for rectal and anal cancer patients”, Medical physics, Vol. 36, p.4349

      [23] Deasy JO, Yang D, El Naqa I, (2007), “Automated estimation of the biophysical target for radiotherapy treatment planning using multimodality image analysis”, Proceeding of 2007 IEEE International Conference on Image Processing, pp:V533-V536

      [24] Drever L, Robinson D, McEwan A, Roa W, (2006), “A local contrast based approach to threshold segmentation for PET target volume delineation” , Med Phys, Vol.33(6), pp.1583–1594.

      [25] Drever L, Roa W, McEwan A, Robinson D, (2007), “Iterative threshold segmentation for PET target volume delineation”, Med Phys, Vol. 34 (4), pp. 1253–1265.

      [26] El Naqa I, Yang D, Apte A, Khullar D, Mutic S, Zheng J, Bradley J, Grigsby P, Deasy J, (2007), “Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning”, Medical Physics, Vol. 34, pp. 4738

      [27] El Naqa I, Bradley J, Deasy J, Biehl K, Laforest R, and Low D, (2004), “Improved analysis of PET images for radiation therapy”, Proceeding of 14th (ICCR) International Conference on the Use of Computers in Radiation Therapy, Seoul, Korea, Vol. 1, pp: 361–363.

      [28] El Naqa I, (2010), “Variational methods for image guide adaptive radiotheraphy”, Proceeding of IEEE Southwest Symposium on Image Analyses & Interpretation (SSIAI), pp: 13-16

      [29] Erdi YE, Mawlawi O, Larson SM, Imbriaco M, Yeung H, Finn R, and Humm JL, (1997), “Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding”, Cancer, Vol.80, pp.2505–2509

      [30] Erdi YE, Rosenzweig K, Erdi AK, Macapinlac HA, Hu Y, Braban LE, Humm JL, Squire OD, Chui C, Larson SM, Yorke ED, (2002), “Radiotherapy treatment planning for patients with non-small cell lung cancer using positron emission tomography (PET)”, Radiotherapy and Oncology, Vol.62(1), pp.51–60.

      [31] Evanko D, (2008), “Two pictures are better than one”, Nat Methods, Vol.5(5), pp.377.

      [32] Fahey F, Kinahan P, Doot R, Kocak M, Thurston H, Poussaint T, (2010), “Variability in pet quantitation within a multicenter consortium”, Medical physics, Vol. 37, pp.3660.

      [33] Ford E, Kinahan P, Hanlon L, Alessio A, Rajendran J, Schwartz D, Phillips M, (2006), “Tumor delineation using PET in head and neck cancers: threshold contouring and lesion volumes”, Med Phys, Vol.33, pp.4280–4288.

      [34] Foster B, Bagci U, Mansoor A, Xu Z, Mollura DJ, ( 2014), “A review on segmentation of positron emission tomography images”, Computers in Biology and Medicine, Vol.50, pp 76–96.

      [35] Fox J, Rengan R, O'Meara W, Yorke E, Erdi Y, Nehmeh S, Leibel S, Rosenzweig K, (2005), “Does registration of PET and planning CT images decrease interobserver and intraobserver variation in delineating tumor volumes for non-small-cell lung cancer?”, Int J Radiat Oncol Biol Phys, Vol.62(1), pp.70–75

      [36] Geets X, Lee J, Bol A, Lonneux M, Grégoire V, (2007), “A gradient-based method for segmenting fdg-pet images: methodology and validation”, European journal of nuclear medicine and molecular imaging, Vol. 34, p.1427–1438.

      [37] 87Grady L, (2006), “Random Walks for Image Segmentation”, IEEE Transactions on Pattern Analysis and machine Intelligence, Vol.28, No.11, pp.1768–1783.

      [38] Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D, (2009), “A fuzzy locally adaptive bayesian segmentation approach for volume determination in pet”, Medical Imaging, IEEE Transactions, Vol. 28, p.881–893.

      [39] Han D, (2011), Globally optimal tumor segmentation in PET-CT images: A graph based co-segmentation method, Lecture notes in computer science

      [40] Han D, Bayouth J, Song Q, Taurani A, Sonka M, Buatti J, Wu X, (2011), “Globally optimal tumor segmentation in PET-CT images: A graph-based co-segmentation method”, Information Processing in Medical Imaging, pp.245–256.

      [41] Harary F, (1994), Graph Theory, ABP

      [42] Hong R, Halama J, Bova D, Sethi A, Emami B, (2007), “Correlation of PET standard uptake value and ct window-level thresholds for target delineation in CT-based radiation treatment planning”, Int J Radiat Oncol Biol Phys, Vol.67(3), pp.720–726.

      [43] Horn BKP, (1986), Robot Vision, MIT Press, pp:69-71.

      [44] Hsu C, Liu C, Chen C, (2008), “Automatic segmentation of liver PET images”, Computerized Medical Imaging and Graphics”, Vol. 32(7), pp.601–610.

      [45] Jentzen W, Freudenberg L, Eising E, Heinze M, Brandau W, Bockisch A, (2007), “Segmentation of PET volumes by iterative image thresholding,” J. Nucl. Med, Vol. 48 (1), pp. 108–114.

      [46] Judenhofer MS, et al, (2008), “Simultaneous PET-MRI: a new approach for functional and morphological imaging”, Nature Medicine, Vol.14, pp.459–465.

      [47] Ju W, Xiang D, Zhang B, Wang L, Kopriva I, Chen X, (2015), “Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images”, IEEE Transactions on Image Processing, pp 1057-7149

      [48] Kanakatte A, Gubbi J, Srinivasan B, Mani N, Kron T, Binns D, Palaniswami M, (2008), “Pulmonary tumor volume delineation in PET images using deformable models”, Proceeding of Engineering in Medicine and Biology Society, 30th Annual International Conference of the IEEE, pp. 3118–3121.

      [49] Koshy M, Paulino A, Howell R, Schuster D, Halkar R, Davis L, (2005), “F-18 FDG PET-CT fusion in radiotherapy treatment planning for head and neck cancer”, Head & neck, Vol. 27, pp. 494–502.

      [50] Kostakoglu L, Agress HJr, Goldsmith S, (2003), “Clinical role of FDG PET in evaluation of cancer patients”, Radiographics, Vol.23(2), pp.315–340.

      [51] Lardinois D, Weder W, Hany T, Kamel E, Korom S, Seifert B, von Schulthess G, Steinert H, (2003), “Staging of non-small-cell lung cancer with integrated positron-emission tomography and computed tomography”, New Engl J Med, Vol.348(25), pp.2500–2507.

      [52] Li H, Thorstad WL, Biehl KJ, Laforest R, Su Y, Shoghi KI, Donnelly ED, Low DA, Lu W, (2008), “A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours”, Medical Physics, Vol. 35, pp. 3711–3721.

      [53] Lurig C, Kobbelt L, Ertl T, (2000), “Hierachical solutions for the deformable surface problem in visualization”, Graphical Models, Vol 62, pp.2-18.

      [54] MacManus MM, Nestle U, Rosenzweig K, Carrio I, Messa C, Belohlavek O, Danna M, Inoue T, Deniaud-Alexandre E, Schipani S, (2009), “Use of PET and PET/CT for radiation therapy planning: Iaea expert report 2006–2007”, Radiother Oncol, Vol.1, pp.85–94.

      [55] Markel D, Caldwell C, Alasti H, Soliman H, Ung Y, Lee J, Sun A, (2013), “Automatic segmentation of lung carcinoma using 3d texture features in 18-FDG PET/CT”, International Journal of Molecular Imaging, Vol. 2013, ID. 980769 http://dx.doi.org/10.1155/2013/980769.

      [56] Matheoud R, Della Monica P, Secco C, Loi G, Krengli M, Inglese E, Brambilla M, 2011, “Influence of different contributions of scatter and attenuation on the threshold values in contrast-based algorithms for volume segmentation”, Phys. Medica, Vol. 27 (1), pp.44–51.

      [57] Miller TR and Grigsby PW, (2002), “Measurement of tumor volume by PET to evaluate prognosis in patients with advanced cervical cancer treated by radiation therapy”, Int J Radiat Oncol Biol Phys, Vol.53, pp.353–359.

      [58] Montgomery D, Amira A, Zaidi H, (2007), “Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model”, Med Phys, Vol.34(2), pp.722–736.

      [59] Munley MT, Marks LB, Scarfone C, Sibley GS, Patz EF, Turkington TG, Jaszczak RJ, Gilland DR, Anscher MS, and Coleman RE, (1999), “Multimodality nuclear medicine imaging in three-dimensional radiation treatment planning for lung cancer: Challenges and prospects”, Lung Cancer, Vol.23, No.2, pp.105–14.

      [60] Murakami R et al, (2007), “Impact of FDG-PET/CT imaging on nodal staging for head-and-neck squamous cell carcinoma”, Int J Radiat Oncol Biol Phys, Vol.68, pp.377–388.

      [61] Nehmeh S, El-Zeftawy H, Greco C, Schwartz J, Erdi Y, Kirov A, Schmidtlein C, Gyau A, Larson S, Humm J, (2009), “An iterative technique to segment PET lesions using a monte carlo based mathematical model”, Med Phys, Vol.36(10), pp.4803–4809.

      [62] Nestle U, Kremp S, Grosu A, (2006), “Practical integration of 18f-FDG-PET and PET-CT in the planning of radiotherapy for non-small cell lung cancer (nsclc): the technical basis, icru-target volumes, problems, perspectives”, Radiother Oncol, Vol.81(2), pp.209–225.

      [63] Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rübe C, Kirsch C, (2005), “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, Vol.46, pp.1342–1348.

      [64] Osher S and Fedkiw RP, 2003, Level Set Methods and Dynamic Implicit Surfaces, Springer, New York

      [65] Otsu N, (1975), A Threshold Selection Method from Gray-Level Histograms, Automatica, pp:23-27.

      [66] Otsu N, (1979), “A threshold selection method from gray-level histogram,” IEEE Trans-actions on Systems, Man, and Cybernetics, Vol. 9, pp: 62–66.

      [67] Paulino AC, Koshy M, Howell R, Schuster D, Daivs LW, (2005), “Comparison of CT- and FDG-PET-defined gross tumor volume in intensity-modulated radiotherapy for head-and-neck cancer”, International Journal of Radiation Oncology, Biology, Physics, Vol.61, pp.1385–1392.

      [68] Prieto E, Lecumberri P, Pagola M, Gómez M, Bilbao I, Ecay M, Peñuelas I, Martí-Climent J, (2012), “Twelve automated thresholding methods for segmentation of pet images: a phantom study”, Physics in Medicine and Biology, Vol. 57, pp. 3963.

      [69] Riegel A, Berson A, Destian S, et al, (2006), “Variability of gross tumor volume delineation in head-and-neck cancer using CT and PET/CT fusion”, International Journal of Radiation Oncology, Biology and Physics, Vol.65(3), pp.726-732

      [70] Rossi F and Abd Rahni AA, (2015), “Combination of low level processing and active contour techniques for semi-automated volumetric lung lesion segmentation from thoracic CT images”, Proceedings of IEEE Student Symposium in Biomedical Engineering & Sciences (ISSBES) Malaysia, pp: 26-30, http://dx.doi.org/10.1109/ISSBES.2015.7435887

      [71] Rossi F, Mokri SS, and Abd. Rahni AA, (2017), “Development of a semi-automated combined PET and CT lung Lesion segmentation framework”, Proceedings of SPIE - Progress in Biomedical Optics and Imaging, Vol.10137B. http://dx.doi.org/10.1117/12.2256808

      [72] Scarfone C, Lavely WC, Cmelak AJ, Delbeke D, Martin WH, Billheimer D, Hallaha DE, (2004), “Prospective feasibility trial of radiotherapy target definition for head and neck cancer using 3-dimensional PET and CT imaging”, Journal of Nuclear Medicine, Vol.45(4), pp.543–552.

      [73] Schaefer A, Kremp S, Hellwig D, Rübe C, Kirsch C, Nestle U, (2008), “A contrastoriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient data”, Eur. J. Nucl. Med. Mol. Imaging, Vol. 35 (11), pp.1989–1999

      [74] Schinagl D, Vogel W, Hoffmann A, van Dalen J, Oyen W, Kaanders J, (2007), “Comparison of five segmentation tools for 18f-fluoro-deoxy-glucose–positron emission tomography–based target volume definition in head and neck cancer”, International Journal of Radiation Oncology, Biology and Physics, Vol. 69, pp.1282–1289.

      [75] Seute T, Leffers P, ten Velde G, Twijnstra A, “Detection of brain metastases from small cell lung cancer”, Cancer, (2008), Vol.112 (8), pp.1827–1834.

      [76] Sethian JA, (1999), Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, Vol. 3, Cambridge University Press, Cambridge, United, Kingdom

      [77] Shah J, (1996), “Curve evolution and segmentation functionals: Application to color images”, Proceeding of the International Image Processing Conference, Lausanne, Switzerland, IEEE Int’l Conf. Image Proc., Vol. 9, pp. 461–464.

      [78] Shankar LK, Hoffman JM, Bacharach S, Graham MM, et al, (2006), “Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute Trials”, J Nucl Med, Vol.47, pp.1059–1066.

      [79] Sharif MS, Abbod M, Amira A, Zaidi H, (2010), “Artificial neural network-based system for PET volume segmentation”, Int. Journal of Biomedical Imaging, Vol. 2010, ID. 105610

      [80] Song Q, Bai J, Han D, Bhatia S, Sun W, Rockey W, Bayouth J, Buatti J, Wu X, (2013), “Optimal co-segmentation of tumor in PET-CT images with context information”, IEEE Trans Med Imaging 32 , pp.1685–1697.

      [81] Steenbakkers R, Duppen J, Fitton I, Deurloo K, Zijp L, Comans E, Uitterhoeve A, Rodrigus P, Kramer G, Bussink J, et al, (2006), “Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis”, Int J Radiat Oncol Biol Phys, Vol.64(2), pp.435–448.

      [82] Suri JS, Kecheng L, Singh S, Laxminarayan SN, Xiaolan Z, and Reden L, (2002), “Shape recovery algorithms using level sets in 2-D/3-D medical imagery: A state-of-the-art review,” IEEE Trans. Inf. Technol. Biomed., Vol. 6, pp. 8–28.

      [83] van Dalen J, Hoffmann A, Dicken V, Vogel W, Wiering B, Ruers T, Karssemeijer N, Oyen W, (2007), “A novel iterative method for lesion delineation and volumetric quantification with FDG PET”, Nucl Med Commun, Vol.28(6), pp. 293–485.

      [84] Vees H, Senthamizhchelvan S, Miralbell R, Weber D, Ratib O, Zaidi H, (2009), “Assessment of various strategies for 18 f-FET PET-guided delineation of target volumes in high-grade glioma patients”, Eur. J. Nucl. Med. Mol. Imaging, Vol. 36 (2), pp. 182–193.

      [85] Wang X, Ballangan C, Cui H, et al, (2014), “Lung Tumor Delineation Based on Novel Tumor-Background Likelihood Models in PET-CT Images”, IEEE Transactions on Nuclear Science, Vol. 61(1)

      [86] Xiaa Y, Eberl S, Wena L, Fulhama M, Feng DD, (2012), “Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information,” Computerized Medical Imaging and Graphics, Vol. 36, pp. 47–53.

      [87] Xu C, Pham DL, and Prince JL, (2002), Handbook of Medical Imaging: Medical Image Processing and Analysis, edited by Sonka M and Fitzpatrick JM, SPIE, Bellingham, WA, Vol.2, pp:129–174.

      [88] Yang F, Grigsby P, (2012), “Delineation of FDG-PET tumors from heterogeneous background using spectral clustering”, Eur. J. Radiol., Vol. 81(11), pp. 3535–3541.

      [89] Yu H, Caldwell C, Mah K, Mozeg D, (2009), “Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning”, IEEE Trans. Med. Imaging, Vol. 28 (3), pp. 374–383.

      [90] Zaidi H, Diaz-Gomez M, Boudraa A, Slosman D, (2002), “Fuzzy clustering-based segmented attenuation correction in whole-body pet imaging”, Physics in medicine and biology, Vol. 47, pp.1143

      [91] Zaidi H and El Naqa I, (2010), “Pet-guided delineation of radiation therapy treatment volumes: a survey of image segmentation technique”, European journal of nuclear medicine and molecular imaging, Vol.37, pp.2165–2187.


 

View

Download

Article ID: 18414
 
DOI: 10.14419/ijet.v7i3.32.18414




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.