Abstract
Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addressed.
Similar content being viewed by others
Notes
The first-order derivative suppresses stretching and makes the contour behave like an elastic string. The second-order derivative suppresses bending and makes the model behave like a rigid rod.
References
Townsend DW. Multimodality imaging of structure and function. Phys Med Biol 2008;53:R1–39.
Hasegawa B, Zaidi H. Dual-modality imaging: more than the sum of its components. In: Zaidi H, editor. Quantitative analysis in nuclear medicine imaging. New York: Springer; 2006. p. 35–81.
Bernier J, Hall EJ, Giaccia A. Radiation oncology: a century of achievements. Nat Rev Cancer 2004;4:737–47.
Fenwick JD, Tomé WA, Soisson ET, Mehta MP, Rock Mackie T. Tomotherapy and other innovative IMRT delivery systems. Semin Radiat Oncol 2006;16:199–208.
Ling C, Zhang P, Archambault Y, Bocanek J, Tang G, Losasso T. Commissioning and quality assurance of RapidArc radiotherapy delivery system. Int J Radiat Oncol Biol Phys 2008;72:575–81.
Jäkel O, Karger CP, Debus J. The future of heavy ion radiotherapy. Med Phys 2008;35:5653–63.
ICRU. Prescribing, recording and reporting photon beam therapy. ICRU Report 62. Washington: International Commission on Radiation Units and Measurements; 1999.
Austin-Seymour M, Chen GT, Rosenman J, Michalski J, Lindsley K, Goitein M. Tumor and target delineation: current research and future challenges. Int J Radiat Oncol Biol Phys 1995;33:1041–52.
Evans PM. Anatomical imaging for radiotherapy. Phys Med Biol 2008;53:R151–91.
Papiez L, Langer M. On probabilistically defined margins in radiation therapy. Phys Med Biol 2006;51:3921–39.
Khoo VS, Adams EJ, Saran F, Bedford JL, Perks JR, Warrington AP, et al. A comparison of clinical target volumes determined by CT and MRI for the radiotherapy planning of base of skull meningiomas. Int J Radiat Oncol Biol Phys 2000;46:1309–17.
Chaney E, Ibbott G, Hendee WR. Methods for image segmentation should be standardized and calibrated. Med Phys 2005;32:3507–10.
Ling C, Humm J, Larson S, Amols H, Fuks Z, Leibel S, et al. Towards multidimensional radiotherapy (MD-CRT): biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 2000;47:551–60.
Zaidi H, Alavi A. Current trends in PET and combined (PET/CT and PET/MR) systems design. PET Clin 2007;2:109–23.
Chapman JD, Bradley JD, Eary JF, Haubner R, Larson SM, Michalski JM, et al. Molecular (functional) imaging for radiotherapy applications: an RTOG symposium. Int J Radiat Oncol Biol Phys 2003;55:294–301.
Grégoire V, Haustermans K, Geets X, Roels S, Lonneux M. PET-based treatment planning in radiotherapy: a new standard? J Nucl Med 2007;48:68S–77.
Grosu AL, Piert M, Weber WA, Jeremic B, Picchio M, Schratzenstaller U, et al. Positron emission tomography for radiation treatment planning. Strahlenther Onkol 2005;181:483–99.
Lecchi M, Fossati P, Elisei F, Orecchia R, Lucignani G. Current concepts on imaging in radiotherapy. Eur J Nucl Med Mol Imaging 2008;35:821–37.
Mah D, Chen CC. Image guidance in radiation oncology treatment planning: the role of imaging technologies on the planning process. Semin Nucl Med 2008;38:114–8.
Messa C, Di Muzio N, Picchio M, Gilardi MC, Bettinardi V, Fazio F. PET/CT and radiotherapy. Q J Nucl Med Mol Imaging 2006;50:4–14.
Zaidi H, Vees H, Wissmeyer M. Molecular PET/CT imaging-guided radiation therapy treatment planning. Acad Radiol 2009;16:1108–33.
Olabarriaga SD, Smeulders AW. Interaction in the segmentation of medical images: a survey. Med Image Anal 2001;5:127–42.
Udupa JK, Saha PK. Fuzzy connectedness and image segmentation. Proc IEEE 2003;91:1649–69.
Boudraa A, Zaidi H. Image segmentation techniques in nuclear medicine imaging. In: Zaidi H, editor. Quantitative analysis of nuclear medicine images. New York: Springer; 2006. p. 308–57.
Zaidi H. Medical image segmentation: quo vadis. Comput Methods Programs Biomed 2006;84:63–7.
van Baardwijk A, Baumert BG, Bosmans G, van Kroonenburgh M, Stroobants S, Gregoire V, et al. The current status of FDG-PET in tumour volume definition in radiotherapy treatment planning. Cancer Treat Rev 2006;32:245–60.
Greco C, Rosenzweig K, Cascini GL, Tamburrini O. Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC). Lung Cancer 2007;57:125–34.
Graves EE, Quon A, Loo Jr BW. RT_Image: an open-source tool for investigating PET in radiation oncology. Technol Cancer Res Treat 2007;6:111–21.
Ahn PH, Garg MK. Positron emission tomography/computed tomography for target delineation in head and neck cancers. Semin Nucl Med 2008;38:141–8.
Rahn AN, Baum RP, Adamietz IA, Adams S, Sengupta S, Mose S, et al. Value of 18F fluorodeoxyglucose positron emission tomography in radiotherapy planning of head-neck tumors. Strahlenther Onkol 1998;174:358–64. German.
Munley MT, Marks LB, Scarfone C, Sibley GS, Patz Jr EF, Turkington TG, et al. Multimodality nuclear medicine imaging in three-dimensional radiation treatment planning for lung cancer: challenges and prospects. Lung Cancer 1999;23:105–14.
Gross MW, Weber WA, Feldmann HJ, Bartenstein P, Schwaiger M, Molls M. The value of F-18-fluorodeoxyglucose PET for the 3-D radiation treatment planning of malignant gliomas. Int J Radiat Oncol Biol Phys 1998;41:989–95.
Kiffer JD, Berlangieri SU, Scott AM, Quong G, Feigen M, Schumer W, et al. The contribution of 18F-fluoro-2-deoxy-glucose positron emission tomographic imaging to radiotherapy planning in lung cancer. Lung Cancer 1998;19:167–77.
Scarfone C, Jaszczak RJ, Gilland DR, Greer KL, Munley MT, Marks LB, et al. Quantitative pulmonary single photon emission computed tomography for radiotherapy applications. Med Phys 1999;26:1579–88.
Nestle U, Walter K, Schmidt S, Licht N, Nieder C, Motaref B, et al. 18F-deoxyglucose positron emission tomography (FDG-PET) for the planning of radiotherapy in lung cancer: high impact in patients with atelectasis. Int J Radiat Oncol Biol Phys 1999;44:593–7.
Vanuytsel LJ, Vansteenkiste JF, Stroobants SG, De Leyn PR, De Wever W, Verbeken EK, et al. The impact of 18F-fluoro-2-deoxy–glucose positron emission tomography (FDG-PET) lymph node staging on the radiation treatment volumes in patients with non-small cell lung cancer. Radiother Oncol 2000;55:317–24.
Levivier M, Wikier D, Goldman S, David P, Metens T, Massager N, et al. Integration of the metabolic data of positron emission tomography in the dosimetry planning of radiosurgery with the gamma knife: early experience with brain tumors. Technical note. J Neurosurg 2000;93 Suppl 3:233–8.
Mah K, Caldwell CB, Ung YC, Danjoux CE, Balogh JM, Ganguli SN, et al. The impact of (18)FDG-PET on target and critical organs in CT-based treatment planning of patients with poorly defined non-small-cell lung carcinoma: a prospective study. Int J Radiat Oncol Biol Phys 2002;52:339–50.
Paulino AC, Thorstad WL, Fox T. Role of fusion in radiotherapy treatment planning. Semin Nucl Med 2003;33:238–43.
Scarfone C, Lavely WC, Cmelak AJ, Delbeke D, Martin WH, Billheimer D, et al. Prospective feasibility trial of radiotherapy target definition for head and neck cancer using 3-dimensional PET and CT imaging. J Nucl Med 2004;45:543–52.
Yap JT, Carney JP, Hall NC, Townsend DW. Image-guided cancer therapy using PET/CT. Cancer J 2004;10:221–33.
Bradley JD, Perez CA, Dehdashti F, Siegel BA. Implementing biologic target volumes in radiation treatment planning for non-small cell lung cancer. J Nucl Med 2004;45 Suppl 1:96S–101.
Brunetti J, Caggiano A, Rosenbluth B, Vialotti C. Technical aspects of positron emission tomography/computed tomography fusion planning. Semin Nucl Med 2008;38:129–36.
Pan T, Mawlawi O. PET/CT in radiation oncology. Med Phys 2008;35:4955–66.
Nestle U, Weber W, Hentschel M, Grosu A-L. Biological imaging in radiation therapy: role of positron emission tomography. Phys Med Biol 2009;54:R1–25.
Macapinlac HA. Clinical applications of positron emission tomography/computed tomography treatment planning. Semin Nucl Med 2008;38:137–40.
Czernin J, Allen-Auerbach M, Schelbert HR. Improvements in cancer staging with PET/CT: literature-based evidence as of September 2006. J Nucl Med 2007;48:78S–88.
Bradley J, Thorstad WL, Mutic S, Miller TR, Dehdashti F, Siegel BA, et al. Impact of FDG-PET on radiation therapy volume delineation in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2004;59:78–86.
**ng L, Siebers J, Keall P. Computational challenges for image-guided radiation therapy: framework and current research. Semin Radiat Oncol 2007;17:245–57.
Stroom J, Blaauwgeers H, van Baardwijk A, Boersma L, Lebesque J, Theuws J, et al. Feasibility of pathology-correlated lung imaging for accurate target definition of lung tumors. Int J Radiat Oncol Biol Phys 2007;69:267–75.
Caldwell CB, Mah K, Skinner M, Danjoux CE. Can PET provide the 3D extent of tumor motion for individualized internal target volumes? A phantom study of the limitations of CT and the promise of PET. Int J Radiat Oncol Biol Phys 2003;55:1381–93.
Nestle U, Kremp S, Grosu AL. 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 2006;81:209–25.
Grosu AL, Weber WA, Astner ST, Adam M, Krause BJ, Schwaiger M, et al. 11C-methionine PET improves the target volume delineation of meningiomas treated with stereotactic fractionated radiotherapy. Int J Radiat Oncol Biol Phys 2006;66:339–44.
Kalff V, Hicks RJ, MacManus MP, Binns DS, McKenzie AF, Ware RE, et al. Clinical impact of (18)F fluorodeoxyglucose positron emission tomography in patients with non-small-cell lung cancer: a prospective study. J Clin Oncol 2001;19:111–8.
Caldwell CB, Mah K, Ung YC, Danjoux CE, Balogh JM, Ganguli SN, et al. Observer variation in contouring gross tumor volume in patients with poorly defined non-small-cell lung tumors on CT: the impact of 18FDG-hybrid PET fusion. Int J Radiat Oncol Biol Phys 2001;51:923–31.
Fox JL, Rengan R, O’Meara W, Yorke E, Erdi Y, Nehmeh S, et al. 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 2005;62:70–5.
van Baardwijk A, Bosmans G, Boersma L, Buijsen J, Wanders S, Hochstenbag M, et al. PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes. Int J Radiat Oncol Biol Phys 2007;68:771–8.
Steenbakkers RJHM, Duppen JC, Fitton I, Deurloo KEI, Zijp LJ, Comans EFI, et al. Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. Int J Radiat Oncol Biol Phys 2006;64:435–48.
Sovik A, Malinen E, Olsen DR. Strategies for biologic image-guided dose escalation: a review. Int J Radiat Oncol Biol Phys 2009;73:650–8.
Basu S. Selecting the optimal image segmentation strategy in the era of multitracer multimodality imaging: a critical step for image-guided radiation therapy. Eur J Nucl Med Mol Imaging 2009;36:180–1.
Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med 2007;48:932–45.
Rousset O, Rahmim A, Alavi A, Zaidi H. Partial volume correction strategies in PET. PET Clin 2007;2:235–49.
Rahmim A, Rousset O, Zaidi H. Strategies for motion tracking and correction in PET. PET Clin 2007;2:251–66.
Nehmeh SA, Erdi YE. Respiratory motion in positron emission tomography/computed tomography: a review. Semin Nucl Med 2008;38:167–76.
Li T, Thorndyke B, Schreibmann E, Yang Y, **ng L. Model-based image reconstruction for four-dimensional PET. Med Phys 2006;33:1288–98.
Qiao F, Pan T, Clark J, John W, Mawlawi O. Joint model of motion and anatomy for PET image reconstruction. Med Phys 2007;34:4626–39.
Lamare F, Ledesma Carbayo MJ, Cresson T, Kontaxakis G, Santos A, Cheze Le Rest C, et al. List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations. Phys Med Biol 2007;52:5187–204.
Rahmim A, Dinelle K, Cheng J-C, Shilov MA, Segars WP, Lidstone SC, et al. Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events. IEEE Trans Med Imaging 2008;27:1018–33.
Büther F, Dawood M, Stegger L, Wübbeling F, Schäfers M, Schober O, et al. List mode-driven cardiac and respiratory gating in PET. J Nucl Med 2009;50:674–81.
Rahmim A, Tang J, Zaidi H. Four-dimensional (4D) image reconstruction strategies in dynamic PET: beyond conventional independent frame reconstruction. Med Phys 2009;36:3654–70.
Perez CA. Principles and practice of radiation oncology. 4th ed. Philadelphia: Lippincott Williams & Wilkins; 2004.
Otsu N. A thresholding selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9:62–6.
Reddi SS, Rudin SF, Keshavan HR. An optimal multiple threshold scheme for image segmentation. IEEE Trans Syst Man Cybern 1984;14:661–5.
Kittler J, Illingworth J. Minimum error thresholding. Pattern Recognit 1986;19:41–7.
Pal NR, Pal SK. A review on image segmentation techniques. Pattern Recognit 1993;26:1277–94.
Huang S-C. Anatomy of SUV. Nucl Med Biol 2000;27:643–6.
Keyes JW Jr. SUV: standard uptake value or silly useless value? J Nucl Med 1995;36:1836–9.
Basu S, Zaidi H, Houseni M, Udupa J, Acton P, Torigian D, et al. Novel quantitative techniques for assessing regional and global function and structure based on modern imaging modalities: implications for normal variation, aging and diseased states. Semin Nucl Med 2007;37:223–39.
Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med 2009;50:11S–20.
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;80:2505–9.
Miller TR, Grigsby PW. 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 2002;53:353–9.
Biehl KJ, Kong FM, Dehdashti F, ** 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.
Ford EC, Kinahan PE, Hanlon L, Alessio A, Rajendran J, Schwartz DL, et al. Tumor delineation using PET in head and neck cancers: threshold contouring and lesion volumes. Med Phys 2006;33:4280–8.
Zaidi H. Organ volume estimation using SPECT. IEEE Trans Nucl Sci 1996;43:2174–82.
Yaremko B, Riauka T, Robinson D, Murray B, Alexander A, McEwan A, et al. Thresholding in PET images of static and moving targets. Phys Med Biol 2005;50:5969–82.
Paulino AC, Koshy M, Howell R, Schuster D, Davis LW. Comparison of CT- and FDG-PET-defined gross tumor volume in intensity-modulated radiotherapy for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2005;61:1385–92.
Schinagl DA, Vogel WV, Hoffmann AL, van Dalen JA, Oyen WJ, Kaanders JH. Comparison of five segmentation tools for 18F-fluoro-deoxy-glucose-positron emission tomography-based target volume definition in head and neck cancer. Int J Radiat Oncol Biol Phys 2007;69:1282–9.
Vees H, Senthamizhchelvan S, Miralbell R, Weber D, Ratib O, Zaidi H. Assessment of various strategies for 18F-FET PET-guided delineation of target volumes in high-grade glioma patients. Eur J Nucl Med Mol Imaging 2009;36:182–93.
Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging 2009;28:881–93.
Black QC, Grills IS, Kestin LL, Wong CY, Wong JW, Martinez AA, et al. Defining a radiotherapy target with positron emission tomography. Int J Radiat Oncol Biol Phys 2004;60:1272–82.
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.
Brambilla M, Matheoud R, Secco C, Loi G, Krengli M, Inglese E. Threshold segmentation for PET target volume delineation in radiation treatment planning: the role of target-to-background ratio and target size. Med Phys 2008;35:1207–13.
Drever L, Robinson DM, McEwan A, Roa W. A local contrast based approach to threshold segmentation for PET target volume delineation. Med Phys 2006;33:1583–94.
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;46:1342–8.
Schaefer A, Kremp S, Hellwig D, Rübe C, Kirsch C-M, Nestle U. A contrast-oriented 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 2008;35:1989–99.
Jentzen W, Freudenberg L, Eising EG, Heinze M, Brandau W, Bockisch A. Segmentation of PET volumes by iterative image thresholding. J Nucl Med 2007;48:108–14.
Nehmeh SA, El-Zeftawy H, Greco C, Schwartz J, Erdi YE, Kirov A, et al. An iterative technique to segment PET lesions using a Monte Carlo based mathematical model. Med Phys 2009;36:4803–9.
Greco C, Nehmeh SA, Schöder H, Gönen M, Raphael B, Stambuk HE, et al. Evaluation of different methods of 18F-FDG-PET target volume delineation in the radiotherapy of head and neck cancer. Am J Clin Oncol 2008;31:439–45.
Marr D, Hildreth E. Theory of edge detection. Proc R Soc Lond B Biol Sci 1980;207:187–217.
Huertas A, Medioni G. Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks. IEEE Trans Pattern Anal Mach Intell 1986;8:651–64.
Canny JF. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986;8:679–98.
Drever LA, Roa W, McEwan A, Robinson D. Comparison of three image segmentation techniques for target volume delineation in positron emission tomography. J Appl Clin Med Phys 2007;8:93–109.
Geets X, Lee J, 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.
El Naqa I, Bradley J, Deasy J, Biehl K, Laforest R, Low D. Improved analysis of PET images for radiation therapy. 14th International Conference on the Use of Computers in Radiation Therapy. Seoul, Korea; 2004. pp 361–63.
Hsu C-Y, Liu C-Y, Chen C-M. Automatic segmentation of liver PET images. Comput Med Imaging Graph 2008;32:601–10.
Li H, Thorstad WL, Biehl KJ, Laforest R, Su Y, Shoghi KI, et al. A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours. Med Phys 2008;35:3711–21. Erratum. pp 5958.
Sethian JA. Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and material science. 2nd ed. Cambridge: Cambridge University Press; 1999.
Xu C, Pham DL, Prince JL. Image segmentation using deformable models. In: Sonka M, Fitzpatrick JM, editors. Handbook of medical imaging: medical image processing and analysis. Bellingham: SPIE Press; 2002. pp. 129–74.
Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis 1988;1:321–31.
Kass M, Witkin A, Terzopoulos. Snakes: active contour models. First International Conference on Computer Vision. London; 1987. pp. 259–68.
Liang J, McInerney T, Terzopoulos D. United snakes. Med Image Anal 2006;10:215–33.
Xu C, Prince JL. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 1998;7:359–69.
Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 1988;79:12–49.
Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley; 2001.
Jain AK, Duin RPW, Mao J. Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 2000;22:4–37.
Clarke LP, Velthuizen RP, Phuphanich S, Schellenberg JD, Arrington JA, Silbiger M. MRI: stability of three supervised segmentation techniques. Magn Reson Imaging 1993;11:95–106.
Vaidyanathan M, Clarke LP, Velthuizen RP, Phuphanich S, Bensaid AM, Hall LO, et al. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Magn Reson Imaging 1995;13:719–28.
Suri JS, Singh S, Reden L. Computer vision and pattern recognition techniques for 2-D and 3-D MR cerebral cortical segmentation (part I): a state-of-the-art review. Pattern Anal Appl 2002;5:46–76.
El Naqa I, Yang Y. Techniques in the detection of microcalcification (MC) clusters in digital mammograms. In: Leondes T, editor. Medical imaging systems: technology and applications. Singapore: World Scientific Publishing Co. Pte. Ltd.; 2005. pp. 15–36.
Boudraa AE, Champier J, Cinotti L, Bordet JC, Lavenne F, Mallet JJ. Delineation and quantitation of brain lesions by fuzzy clustering in positron emission tomography. Comput Med Imaging Graph 1996;20:31–41.
Zhu W, Jiang T. Automation segmentation of PET image for brain tumors. IEEE Nucl Sci Symp Conf Rec 2003;4:2627–29.
Kim J, Wen L, Eberl S, Fulton R, Feng DD. Use of anatomical priors in the segmentation of PET lung tumor images. IEEE Nucl Sci Symp Conf Rec 2007;4:4242–45.
Belhassen S and Zaidi H. A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys 2010;37:1309–1324.
Zaidi H, Diaz-Gomez M, Boudraa AO, Slosman DO. Fuzzy clustering-based segmented attenuation correction in whole-body PET imaging. Phys Med Biol 2002;47:1143–60.
Acton PD, Pilowsky LS, Kung HF, Ell PJ. Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering. Eur J Nucl Med 1999;26:581–90.
Bezdek JC, Hall LO, Clark MC, Goldgof DB, Clarke LP. Medical image analysis with fuzzy models. Stat Methods Med Res 1997;6:191–214.
Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv 1999;31:264–323.
De Luca A, Termini S. A definition of non-probabilistic entropy in the setting of fuzzy sets theory. Inform Control 1972;20:301–12.
Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 1992;3:672–82.
Pham DL, Prince JL. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognit Lett 1999;20:57–68.
Janssen MH, Aerts HJ, Ollers MC, Bosmans G, Lee JA, Buijsen J, et al. Tumor delineation based on time-activity curve differences assessed with dynamic fluorodeoxyglucose positron emission tomography-computed tomography in rectal cancer patients. Int J Radiat Oncol Biol Phys 2009;73:456–65.
Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990;12:629–39.
Montgomery D, Amira A, Zaidi H. Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model. Med Phys 2007;34:722–36.
Aristophanous M, Penney BC, Martel MK, Pelizzari CA. A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography. Med Phys 2007;34:4223–35.
Van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imaging 1999;18:897–908.
Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839–51.
Hatt M, Lamare F, Boussion N, Turzo A, Collet C, Salzenstein F, et al. Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET. Phys Med Biol 2007;52:3467–91.
Salzenstein F, Pieczynski W. Parameter estimation in hidden fuzzy Markovian fields and image segmentation. Graph Models Image Process 1997;59:205–20.
Long DT, King MA, Sheehan J. Comparative evaluation of image segmentation methods for volume quantitation in SPECT. Med Phys 1992;19:483–9.
Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process 2001;10:266–77.
Guido A, Fuccio L, Rombi B, Castellucci P, Cecconi A, Bunkheila F, et al. Combined (18)F-FDG-PET/CT imaging in radiotherapy target delineation for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2009;73:759–63.
Ciernik IF, Dizendorf E, Baumert BG, Reiner B, Burger C, Davis JB, et al. Radiation treatment planning with an integrated positron emission and computer tomography (PET/CT): a feasibility study. Int J Radiat Oncol Biol Phys 2003;57:853–63.
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;34:4738–49.
Jannin P, Krupinski E, Warfield S. Validation in medical image processing. IEEE Trans Med Imaging 2006;25:1405–9.
Slomka P, Baum R. Multimodality image registration with software: state-of-the-art. Eur J Nucl Med Mol Imaging 2009;36:S44–55.
Fiorino C, Reni M, Bolognesi A, Cattaneo GM, Calandrino R. Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning. Radiother Oncol 1998;47:285–92.
Giraud P, Elles S, Helfre S, De Rycke Y, Servois V, Carette MF, et al. Conformal radiotherapy for lung cancer: different delineation of the gross tumor volume (GTV) by radiologists and radiation oncologists. Radiother Oncol 2002;62:27–36.
Belhassen S, Llina Fuentes CS, Dekker A, De Ruysscher D, Ratib O, Zaidi H. Comparative methods for 18F-FDG PET-based delineation of target volumes in non-small-cell lung cancer [abstract]. J Nucl Med 2009;50:27P.
Boucher L, Rodrigue S, Lecomte R, Bénard F. Respiratory gating for 3-dimensional PET of the thorax: feasibility and initial results. J Nucl Med 2004;45:214–9.
El Naqa I, Low DA, Bradley JD, Vicic M, Deasy JO. Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods. Med Phys 2006;33:3587–600.
Turkington TG, Degrado TR, Sampson WH. Small spheres for lesion detection phantoms. IEEE Nucl Sci Symp Conf Rec 2001;4:2234–37.
Bazañez-Borgert M, Bundschuh RA, Herz M, Martínez MJ, Schwaiger M, Ziegler SI. Radioactive spheres without inactive wall for lesion simulation in PET. Z Med Phys 2008;18:37–42.
Zaidi H, Xu XG. Computational anthropomorphic models of the human anatomy: the path to realistic Monte Carlo modeling in radiological sciences. Annu Rev Biomed Eng 2007;9:471–500.
Zaidi H, Tsui BMW. Review of computational anthropomorphic anatomical and physiological models. Proc IEEE 2009;97:1938–53.
Segars WP. Development and application of the new dynamic NURBS-based cardiac-torso (NCAT) phantom [PhD Thesis]: University of North Carolina, Chapel Hill, NC, USA; 2001.
Piegl L, Tiller W. The NURBS book. New York: Springer; 1997.
Segars WP, Tsui BMW. MCAT to XCAT: the evolution of 4D computerized phantoms for imaging research. Proc IEEE 2009;97:1954–68.
Aristophanous M, Penney BC, Pelizzari CA. The development and testing of a digital PET phantom for the evaluation of tumor volume segmentation techniques. Med Phys 2008;35:3331–42.
Tomei S, Reilhac A, Visvikis D, Odet C, Giammarile F, Mognetti T, et al. Development of a database of realistic simulated whole body 18F-FDG images for lymphoma. Proc IEEE Nuclear Science Symposium and Medical Imaging Conference. Dresden, Germany: IEEE; 2008. pp. 4958–63.
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;97:2026–38.
Zaidi H, Herrmann Scheurer A, Morel C. An object-oriented Monte Carlo simulator for 3D positron tomographs. Comput Methods Programs Biomed 1999;58:133–45.
Jan S, Santin G, Strul D, Staelens S, Assie K, Autret D, et al. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol 2004;49:4543–61.
Harrison RL, Vannoy SD, Haynor DR, Gillispie SB, Kaplan MS, Lewellen TK. Preliminary experience with the photon history generator module for a public-domain simulation system for emission tomography. Records of IEEE Nuclear Science Symposium and Medical Imaging Conference; 1993. pp. 1154–58.
Ay M, Zaidi H. Development and validation of MCNP4C-based Monte Carlo simulator for fan- and cone-beam x-ray CT. Phys Med Biol 2005;50:4863–85.
Kyriakou Y, Riedel T, Kalender WA. Combining deterministic and Monte Carlo calculations for fast estimation of scatter intensities in CT. Phys Med Biol 2006;51:4567–86.
Malusek A, Sandborg M, Carlsson GA. CTmod-A toolkit for Monte Carlo simulation of projections including scatter in computed tomography. Comput Methods Programs Biomed 2008;90:167–78.
Ay M, Zaidi H. Assessment of errors caused by X-ray scatter and use of contrast medium when using CT-based attenuation correction in PET. Eur J Nucl Med Mol Imaging 2006;33:1301–13.
Zhang YJ. A survey on evaluation methods for image segmentation. Pattern Recognit Lett 1996;29:1335–46.
Zou KH, Warfield SK, Bharatha A, Tempany CM, Kaus MR, Haker SJ, et al. Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 2004;11:178–89.
Edwards PJ, Nijmeh AD, McGurk M, Odell E, Fenlon MR, Marsden PK, et al. Validation of PET imaging by alignment to histology slices. Int Conf Med Image Comput Comput Assist Interv 2005;8:968–75.
Daisne JF, Duprez T, Weynand B, Lonneux M, Hamoir M, Reychler H, et al. Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology 2004;233:93–100.
Mamede M, Abreu ELP, Oliva MR, Nosé V, Mamon H, Gerbaudo VH. FDG-PET/CT tumor segmentation-derived indices of metabolic activity to assess response to neoadjuvant therapy and progression-free survival in esophageal cancer: correlation with histopathology results. Am J Clin Oncol 2007;30:377–88.
Burri RJ, Rangaswamy B, Kostakoglu L, Hoch B, Genden EM, Som PM, et al. Correlation of positron emission tomography standard uptake value and pathologic specimen size in cancer of the head and neck. Int J Radiat Oncol Biol Phys 2008;71:682–8.
Venel Y, Garhi H, de Muret A, Baulieu J-L, Barillot I, Prunier-Aesch C. Comparaison de six méthodes de segmentation du volume tumoral sur la 18F-FDG TEP-TDM avec le volume de référence anatomopathologique dans les cancers bronchopulmonaires non à petites cellules. Médecine Nucléaire 2008;32:339–53.
Seitz O, Chambron-Pinho N, Middendorp M, Sader R, Mack M, Vogl TJ, et al. 18F-Fluorodeoxyglucose-PET/CT to evaluate tumor, nodal disease, and gross tumor volume of oropharyngeal and oral cavity cancer: comparison with MR imaging and validation with surgical specimen. Neuroradiology 2009;51:677–86.
Yu J, Li X, **ng L, Mu D, Fu Z, Sun X, et al. Comparison of tumor volumes as determined by pathologic examination and FDG-PET/CT images of non-small-cell lung cancer: a pilot study. Int J Radiat Oncol Biol Phys 2009;75:1468–74.
Yu HM, Liu YF, Hou M, Liu J, Li XN, Yu JM. Evaluation of gross tumor size using CT, (18)F-FDG PET, integrated (18)F-FDG PET/CT and pathological analysis in non-small cell lung cancer. Eur J Radiol 2009;75:1468–74.
Dahele M, Hwang D, Peressotti C, Sun L, Kusano M, Okhai S, et al. Develo** a methodology for three-dimensional correlation of PET-CT images and whole-mount histopathology in non-small-cell lung cancer. Curr Oncol 2008;15:62–9.
Christian N, Lee JA, Bol A, De Bast M, Jordan B, Grégoire V. The limitation of PET imaging for biological adaptive-IMRT assessed in animal models. Radiother Oncol 2009;91:101–16.
Geets X, Daisne JF, Gregoire V, Hamoir M, Lonneux M. Role of 11-C-methionine positron emission tomography for the delineation of the tumor volume in pharyngo-laryngeal squamous cell carcinoma: comparison with FDG-PET and CT. Radiother Oncol 2004;71:267–73.
Topkan E, Yavuz AA, Aydin M, Onal C, Yapar F, Yavuz MN. Comparison of CT and PET-CT based planning of radiation therapy in locally advanced pancreatic carcinoma. J Exp Clin Cancer Res 2008;27:41.
Ford EC, Lavely WC, Frassica DA, Myers LT, Asrari F, Wahl RL, et al. Comparison of FDG-PET/CT and CT for delineation of lumpectomy cavity for partial breast irradiation. Int J Radiat Oncol Biol Phys 2008;71:595–602.
Visser EP, Philippens MEP, Kienhorst L, Kaanders JHAM, Corstens FHM, de Geus-Oei L-F, et al. Comparison of tumor volumes derived from glucose metabolic rate maps and SUV maps in dynamic 18F-FDG PET. J Nucl Med 2008;49:892–8.
Grgic A, Nestle U, Schaefer-Schuler A, Kremp S, Kirsch CM, Hellwig D. FDG-PET-based radiotherapy planning in lung cancer: optimum breathing protocol and patient positioning—an intraindividual comparison. Int J Radiat Oncol Biol Phys 2009;73:103–11.
Zou KH, Wells WM, Kikinis R, Warfield SK. Three validation metrics for automated probabilistic image segmentation of brain tumours. Stat Med 2004;23:1259–82.
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: in press.
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307–10.
Swensson RG. Unified measurement of observer performance in detecting and localizing target objects on images. Med Phys 1996;23:1709–25.
Zou KH, Warfield SK, Fielding JR, Tempany CM, William MW, Kaus MR, et al. Statistical validation based on parametric receiver operating characteristic analysis of continuous classification data. Acad Radiol 2003;10:1359–68.
Henkelman RM, Kay I, Bronskill MJ. Receiver operator characteristic (ROC) analysis without truth. Med Decis Making 1990;10:24–9.
Beiden SV, Campbell G, Meier KL, Wagner RF. On the problem of ROC analysis without truth: the EM algorithm and the information matrix. Proc SPIE 2000;3981:126–34.
Hoppin JW, Kupinski MA, Kastis GA, Clarkson E, Barrett HH. Objective comparison of quantitative imaging modalities without the use of a gold standard. IEEE Trans Med Imaging 2002;21:441–9.
Kupinski MA, Hoppin JW, Clarkson E, Barrett HH, Kastis GA. Estimation in medical imaging without a gold standard. Acad Radiol 2002;9:290–7.
Hoppin JW, Kupinski MA, Wilson DW, Peterson T, Gershman B, Kastis G, et al. Evaluating estimation techniques in medical imaging without a gold standard: experimental validation. Proc SPIE 2003;5034:230–7.
Zaidi H, Ruest T, Schoenahl F, Montandon M-L. Comparative evaluation of statistical brain MR image segmentation algorithms and their impact on partial volume effect correction in PET. Neuroimage 2006;32:1591–607.
Maes F, Vandermeulen D, Suetens P. Medical image registration using mutual information. Proc IEEE 2003;91:1699–722.
Viola P. Alignment by maximization of mutual information. [PhD Thesis]. Massachusetts Institute of Technology; Cambridge, 1995.
Holden M, Hill DL, Denton ER, Jarosz JM, Cox TC, Rohlfing T, et al. Voxel similarity measures for 3-D serial MR brain image registration. IEEE Trans Med Imaging 2000;19:94–102.
Aerts HJ, Bosmans G, van Baardwijk AA, Dekker AL, Oellers MC, Lambin P, et al. Stability of (18)F-deoxyglucose uptake locations within tumor during radiotherapy for NSCLC: a prospective study. Int J Radiat Oncol Biol Phys 2008;71:1402–7.
Kumar R, Dhanpathi H, Basu S, Rubello D, Fanti S, Alavi A. Oncologic PET tracers beyond [(18)F]FDG and the novel quantitative approaches in PET imaging. Q J Nucl Med Mol Imaging 2008;52:50–65.
Lewis JS, Welch MJ, Tang L. Workshop on the production, application and clinical translation of “non-standard” PET nuclides: a meeting report. Q J Nucl Med Mol Imaging 2008;52:101–6.
Bading JR, Shields AF. Imaging of cell proliferation: status and prospects. J Nucl Med 2008;49 Suppl 2:64S–80.
Dunphy MPS, Lewis JS. Radiopharmaceuticals in preclinical and clinical development for monitoring of therapy with PET. J Nucl Med 2009;50:106S–21.
Koch CJ, Evans SM. Non-invasive PET and SPECT imaging of tissue hypoxia using isotopically labeled 2-nitroimidazoles. Adv Exp Med Biol 2003;510:285–92.
Grosu AL, Souvatzoglou M, Röper B, Dobritz M, Wiedenmann N, Jacob V, et al. Hypoxia imaging with FAZA-PET and theoretical considerations with regard to dose painting for individualization of radiotherapy in patients with head and neck cancer. Int J Radiat Oncol Biol Phys 2007;69:541–51.
Jager PL, Chirakal R, Marriott CJ, Brouwers AH, Koopmans KP, Gulenchyn KY. 6-L-18F-fluorodihydroxyphenylalanine PET in neuroendocrine tumors: basic aspects and emerging clinical applications. J Nucl Med 2008;49:573–86.
Tang BN, Van Simaeys G, Devriendt D, Sadeghi N, Dewitte O, Massager N, et al. Three-dimensional Gaussian model to define brain metastasis limits on (11)C-methionine PET. Radiother Oncol 2008;89:270–7.
Ciernik IF, Brown DW, Schmid D, Hany T, Egli P, Davis JB. 3D-segmentation of the 18F-choline PET signal for target volume definition in radiation therapy of the prostate. Technol Cancer Res Treat 2007;6:23–30.
Wang H, Vees H, Miralbell R, Wissmeyer M, Steiner C, Ratib O, et al. (18)F-fluorocholine PET-guided target volume delineation techniques for partial prostate re-irradiation in local recurrent prostate cancer. Radiother Oncol 2009;93:220–5.
Weber D, Wang H, Cozzi L, Dipasquale G, Khan H, Ratib O, et al. RapidArc, intensity modulated photon and proton techniques for recurrent prostate cancer in previously irradiated patients: a treatment planning comparison study. Radiat Oncol 2009;4:34.
Patel DA, Chang ST, Goodman KA, Quon A, Thorndyke B, Gambhir SS, et al. Impact of integrated PET/CT on variability of target volume delineation in rectal cancer. Technol Cancer Res Treat 2007;6:31–6.
Weber DC, Zilli T, Buchegger F, Casanova N, Haller G, Rouzaud M, et al. [(18)F]Fluoroethyltyrosine-positron emission tomography-guided radiotherapy for high-grade glioma. Radiat Oncol 2008;3:44.
Zaidi H, Mawlawi O. Simultaneous PET/MR will replace PET/CT as the molecular multimodality imaging platform of choice. Med Phys 2007;34:1525–8.
Pichler BJ, Wehrl HF, Kolb A, Judenhofer MS. Positron emission tomography/magnetic resonance imaging: the next generation of multimodality imaging? Semin Nucl Med 2008;38:199–208.
Hillner BE, Siegel BA, Liu D, Shields AF, Gareen IF, Hanna L, et al. Impact of positron emission tomography/computed tomography and positron emission tomography (PET) alone on expected management of patients with cancer: initial results from the National Oncologic PET Registry. J Clin Oncol 2008;26:2155–61.
Riegel AC, Berson AM, Destian S, Ng T, Tena LB, Mitnick RJ, et al. Variability of gross tumor volume delineation in head-and-neck cancer using CT and PET/CT fusion. Int J Radiat Oncol Biol Phys 2006;65:726–32.
Davis JB, Reiner B, Huser M, Burger C, Szekely G, Ciernik IF. Assessment of (18)F PET signals for automatic target volume definition in radiotherapy treatment planning. Radiother Oncol 2006;80:43–50.
Drever L, Roa W, McEwan A, Robinson D. Iterative threshold segmentation for PET target volume delineation. Med Phys 2007;34:1253–65.
Vauclin S, Doyeux K, Hapdey S, Edet-Sanson A, Vera P, Gardin I. Development of a generic thresholding algorithm for the delineation of 18FDG-PET-positive tissue: application to the comparison of three thresholding models. Phys Med Biol 2009;54:6901–16.
Day E, Betler J, Parda D, Reitz B, Kirichenko A, Mohammadi S, et al. A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients. Med Phys 2009;36:4349–58.
van Dalen JA, Hoffmann AL, Dicken V, Vogel WV, Wiering B, Ruers TJ, et al. A novel iterative method for lesion delineation and volumetric quantification with FDG PET. Nucl Med Commun 2007;28:485–93.
Erdi YE, Rosenzweig K, Erdi AK, Macapinlac HA, Hu Y-C, Braban LE, et al. Radiotherapy treatment planning for patients with non-small cell lung cancer using positron emission tomography (PET). Radiother Oncol 2002;62:51–60.
Vrieze O, Haustermans K, De Wever W, Lerut T, Van Cutsem E, Ectors N, et al. Is there a role for FGD-PET in radiotherapy planning in esophageal carcinoma? Radiother Oncol 2004;73:269–75.
van Loon J, Offermann C, Bosmans G, Wanders R, Dekker A, Borger J, et al. 18FDG-PET based radiation planning of mediastinal lymph nodes in limited disease small cell lung cancer changes radiotherapy fields: a planning study. Radiother Oncol 2008;87:49–54.
Breen SL, Publicover J, De Silva S, Pond G, Brock K, O’Sullivan B, et al. Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers. Int J Radiat Oncol Biol Phys 2007;68:763–70.
Schinagl DA, Hoffmann AL, Vogel WV, van Dalen JA, Verstappen SM, Oyen WJ, et al. Can FDG-PET assist in radiotherapy target volume definition of metastatic lymph nodes in head-and-neck cancer? Radiother Oncol 2009;91:95–100.
Murakami R, Uozumi H, Hirai T, Nishimura R, Katsuragawa S, Shiraishi S, et al. Impact of FDG-PET/CT fused imaging on tumor volume assessment of head-and-neck squamous cell carcinoma: intermethod and interobserver variations. Acta Radiol 2008;49:693–9.
El-Bassiouni M, Ciernik IF, Davis JB, El-Attar I, Reiner B, Burger C, et al. [18FDG] PET-CT-based intensity-modulated radiotherapy treatment planning of head and neck cancer. Int J Radiat Oncol Biol Phys 2007;69:286–93.
Deantonio L, Beldi D, Gambaro G, Loi G, Brambilla M, Inglese E, et al. FDG-PET/CT imaging for staging and radiotherapy treatment planning of head and neck carcinoma. Radiat Oncol 2008;3:29.
Acknowledgements
This work was supported by the Swiss National Science Foundation under grant SNSF 3152A0-102143 and the National Institutes of Health under grant 1K25CA128809-01A1.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zaidi, H., El Naqa, I. PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur J Nucl Med Mol Imaging 37, 2165–2187 (2010). https://doi.org/10.1007/s00259-010-1423-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00259-010-1423-3