Classification and evaluation strategies of auto‐segmentation approaches for PET: Report of AAPM task group No. 211
dc.contributor.author | Hatt, Mathieu | |
dc.contributor.author | Lee, John A. | |
dc.contributor.author | Schmidtlein, Charles R. | |
dc.contributor.author | Naqa, Issam El | |
dc.contributor.author | Caldwell, Curtis | |
dc.contributor.author | De Bernardi, Elisabetta | |
dc.contributor.author | Lu, Wei | |
dc.contributor.author | Das, Shiva | |
dc.contributor.author | Geets, Xavier | |
dc.contributor.author | Gregoire, Vincent | |
dc.contributor.author | Jeraj, Robert | |
dc.contributor.author | MacManus, Michael P. | |
dc.contributor.author | Mawlawi, Osama R. | |
dc.contributor.author | Nestle, Ursula | |
dc.contributor.author | Pugachev, Andrei B. | |
dc.contributor.author | Schöder, Heiko | |
dc.contributor.author | Shepherd, Tony | |
dc.contributor.author | Spezi, Emiliano | |
dc.contributor.author | Visvikis, Dimitris | |
dc.contributor.author | Zaidi, Habib | |
dc.contributor.author | Kirov, Assen S. | |
dc.date.accessioned | 2017-06-16T20:14:02Z | |
dc.date.available | 2018-08-07T15:51:22Z | en |
dc.date.issued | 2017-06 | |
dc.identifier.citation | Hatt, Mathieu; Lee, John A.; Schmidtlein, Charles R.; Naqa, Issam El; Caldwell, Curtis; De Bernardi, Elisabetta; Lu, Wei; Das, Shiva; Geets, Xavier; Gregoire, Vincent; Jeraj, Robert; MacManus, Michael P.; Mawlawi, Osama R.; Nestle, Ursula; Pugachev, Andrei B.; Schöder, Heiko ; Shepherd, Tony; Spezi, Emiliano; Visvikis, Dimitris; Zaidi, Habib; Kirov, Assen S. (2017). "Classification and evaluation strategies of auto‐segmentation approaches for PET: Report of AAPM task group No. 211." Medical Physics 44(6): e1-e42. | |
dc.identifier.issn | 0094-2405 | |
dc.identifier.issn | 2473-4209 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/137483 | |
dc.publisher | Springer | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | treatment planning | |
dc.subject.other | treatment assessment | |
dc.subject.other | PET segmentation | |
dc.subject.other | PET/CT | |
dc.title | Classification and evaluation strategies of auto‐segmentation approaches for PET: Report of AAPM task group No. 211 | |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Medicine (General) | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/137483/1/mp12124_am.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/137483/2/mp12124.pdf | |
dc.identifier.doi | 10.1002/mp.12124 | |
dc.identifier.source | Medical Physics | |
dc.identifier.citedreference | Yu DF, Fessler JA. Edge‐preserving tomographic reconstruction with nonlocal regularization. IEEE Trans Med Imaging. 2002; 21: 159 – 173. | |
dc.identifier.citedreference | Mamede M, Abreu ELP, Oliva MR, Nose 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 – 388. | |
dc.identifier.citedreference | Necib H, Garcia C, Wagner A, et al. Detection and characterization of tumor changes in 18F‐FDG PET patient monitoring using parametric imaging. J Nucl Med. 2011; 52: 354 – 361. | |
dc.identifier.citedreference | Mi HM, Petitjean C, Vera P, Ruan S. Joint tumor growth prediction and tumor segmentation on therapeutic follow‐up PET images. Med Image Anal. 2015; 23: 84 – 91. | |
dc.identifier.citedreference | Mi HM, Petitjean C, Dubray B, Vera P, Ruan S. Prediction of lung tumor evolution during radiotherapy in individual patients with PET. IEEE Trans Med Imaging. 2014; 33: 995 – 1003. | |
dc.identifier.citedreference | Sampedro F, Escalera S, Domenech A, Carrio I. A computational framework for cancer response assessment based on oncological PET‐CT scans. Comput Biol Med. 2014; 55: 92 – 99. | |
dc.identifier.citedreference | Obara P, Liu H, Wroblewski K, et al. Quantification of metabolic tumor activity and burden in patients with non‐small‐cell lung cancer: is manual adjustment of semiautomatic gradient‐based measurements necessary? Nucl Med Commun. 2015; 36: 782 – 789. | |
dc.identifier.citedreference | Beichel RR, Van Tol M, Ulrich EJ, et al. Semiautomated segmentation of head and neck cancers in 18F‐FDG PET scans: a just‐enough‐interaction approach. Med Phys. 2016; 43: 2948. | |
dc.identifier.citedreference | Tylski P, Bonniaud G, Decenciere E, et al. 18F‐FDG PET images segmentation using morphological watershed: a phantom study. In: 2006 IEEE Nuclear Science Symposium Conference: 2063 – 2067. | |
dc.identifier.citedreference | Sharif MS, Abbod M, Amira A, Zaidi H. Artificial neural network‐statistical approach for PET volume analysis and classification. Advances in Fuzzy Systems. ID 327861, 2012; 10. | |
dc.identifier.citedreference | De Bernardi E, Fiorani Gallotta F, Gianoli C, Zito F, Gerundini P, Baselli G. ML segmentation strategies for object interference compensation in FDG‐PET lesion quantification. Methods Inf Med. 2010; 49: 537 – 541. | |
dc.identifier.citedreference | Onoma DP, Ruan S, Thureau S, et al. Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D‐locally adaptive random walk algorithm. Comput Med Imaging Graph. 2014; 38: 753 – 763. | |
dc.identifier.citedreference | Mu W, Chen Z, Shen W, et al. A segmentation algorithm for quantitative analysis of heterogeneous tumors of the cervix with 18F‐FDG PET/CT. IEEE Trans Biomed Eng 2015; 62: 2465 – 2479. | |
dc.identifier.citedreference | Lapuyade‐Lahorgue J, Visvikis D, Pradier O, Cheze Le Rest C, Hatt M. SPEQTACLE: an automated generalized fuzzy C‐means algorithm for tumor delineation in PET. Med Phys. 2015; 42: 5720 – 5734. | |
dc.identifier.citedreference | Devic S, Mohammed H, Tomic N, et al. FDG‐PET‐based differential uptake volume histograms: a possible approach towards definition of biological target volumes. Br J Radiol. 2016; 89: 20150388. | |
dc.identifier.citedreference | 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 – 1289. | |
dc.identifier.citedreference | Greco C, Nehmeh SA, Schoder H, 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 – 445. | |
dc.identifier.citedreference | Vees H, Senthamizhchelvan S, Miralbell R, Weber DC, 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 – 193. | |
dc.identifier.citedreference | 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. J Nucl Med 2009; 50: 27P. | |
dc.identifier.citedreference | Dewalle‐Vignion AS, Yeni N, Petyt G, et al. Evaluation of PET volume segmentation methods: comparisons with expert manual delineations. Nucl Med Commun. 2012; 33: 34 – 42. | |
dc.identifier.citedreference | Lacout A, Marcy PY, Giron J, Thariat J. Gradient‐PET based delineation may be improved with combined post contrast high resolution CT scan: in regard to Werner‐Wasik M et al. (Int J Radiat Oncol Biol Phys 2011 Apr 28). Int J Radiat Oncol Biol Phys. 2012; 82: 496; author reply 496‐497. | |
dc.identifier.citedreference | Schinagl DA, Span PN, Van den Hoogen FJ, et al. Pathology‐based validation of FDG PET segmentation tools for volume assessment of lymph node metastases from head and neck cancer. Eur J Nucl Med Mol Imaging. 2013; 40: 1828 – 1835. | |
dc.identifier.citedreference | 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 – 1594. | |
dc.identifier.citedreference | 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 – 6916. | |
dc.identifier.citedreference | Burger IA, Vargas HA, Apte A, et al. PET quantification with a histogram derived total activity metric: superior quantitative consistency compared to total lesion glycolysis with absolute or relative SUV thresholds in phantoms and lung cancer patients. Nucl Med Biol. 2014; 41: 410 – 418. | |
dc.identifier.citedreference | Li G, Schmidtlein CR, Burger IA, Ridge CA, Solomon SB, Humm JL. Assessing and accounting for the impact of respiratory motion on FDG uptake and viable volume for liver lesions in free‐breathing PET using respiration‐suspended PET images as reference. Med Phys. 2014; 41: 091905. | |
dc.identifier.citedreference | Kong F, Machtay M, Bradley J, Ten Haken R, Xiao Y, Matuszak M. RTOG 1106/ACRIN 6697: Randomized phase II trial of individualized adaptive radiotherapy using during‐treatment FDG‐PET/CT and modern technology in locally advanced non‐small cell lung cancer (NSCLC). 2012. | |
dc.identifier.citedreference | Kong FM, Frey KA, Quint LE, et al. A pilot study of [18F]fluorodeoxyglucose positron emission tomography scans during and after radiation‐based therapy in patients with non small‐cell lung cancer. J Clin Oncol. 2007; 25: 3116 – 3123. | |
dc.identifier.citedreference | Drever L, Roa W, McEwan A, Robinson D. Iterative threshold segmentation for PET target volume delineation. Med Phys. 2007; 34: 1253 – 1265. | |
dc.identifier.citedreference | Krak NC, Boellaard R, Hoekstra OS, Twisk JW, Hoekstra CJ, Lammertsma AA. Effects of ROI definition and reconstruction method on quantitative outcome and applicability in a response monitoring trial. Eur J Nucl Med Mol Imaging. 2005; 32: 294 – 301. | |
dc.identifier.citedreference | Burger IA, Vargas HA, Beattie BJ, et al. How to assess background activity: introducing a histogram‐based analysis as a first step for accurate one‐step PET quantification. Nucl Med Commun. 2014; 35: 316 – 324. | |
dc.identifier.citedreference | Vanderhoek M, Perlman SB, Jeraj R. Impact of the definition of peak standardized uptake value on quantification of treatment response. J Nucl Med. 2012; 53: 4 – 11. | |
dc.identifier.citedreference | Miller M, Hutchins G. 3D Anatomically accurate phantoms for PET and SPECT imaging. IEEE Nuclear Science Symposium and Medical Imaging Conference Record, Proceedings paper, M26‐8, 2007; 49: 4252 – 4256. | |
dc.identifier.citedreference | Berthon B, Marshall C, Holmes R, Spezi E. A novel phantom technique for evaluating the performance of PET auto‐segmentation methods in delineating heterogeneous and irregular lesions. EJNMMI Physics. 2015; 2: 13. | |
dc.identifier.citedreference | Le Maitre A, Segars WP, Marache S, et al. Incorporating patient‐specific variability in the simulation of realisticwhole‐body 18F‐FDG distributions for oncology applications. Proc IEEE. 2009; 97: 2026 – 2038. | |
dc.identifier.citedreference | Papadimitroulas P, Loudos G, Le Maitre A, et al. Investigation of realistic PET simulations incorporating tumor patient’s specificity using anthropomorphic models: creation of an oncology database. Med Phys. 2013; 40: 112506. | |
dc.identifier.citedreference | Munkres JR. Topology. ( 2nd ed.). Prentice Hall; 2000. | |
dc.identifier.citedreference | Aspert N, Santa‐Cruz D, Ebrahimi T. Mesh: measuring errors between surfaces using the Hausdorff distance. IEEE Int Conf Multimed Expo (ICME). 2002; 1: 705 – 708. | |
dc.identifier.citedreference | Sharif MS, Abbod M, Amira A, Zaidi H. Artificial neural network‐based system for PET volume segmentation. Int J Biomed Imaging. ID 105610, 2010; 11. | |
dc.identifier.citedreference | MacManus M, Nestle U, Rosenzweig KE, et al. Use of PET and PET/CT for radiation therapy planning: IAEA expert report 2006‐2007. Radiother Oncol. 2009; 91: 85 – 94. | |
dc.identifier.citedreference | Ling CC, Humm J, Larson S, et al. Towards multidimensional radiotherapy (MD‐CRT): biological imaging and biological conformality. Int J Radiat Oncol Biol Phys. 2000; 47: 551 – 560. | |
dc.identifier.citedreference | Huang SC. Anatomy of SUV. Standardized uptake value. Nucl Med Biol. 2000; 27: 643 – 646. | |
dc.identifier.citedreference | Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med. 2009; 50: 11S – 20S. | |
dc.identifier.citedreference | Gambhir SS, Czernin J, Schwimmer J, Silverman DH, Coleman RE, Phelps ME. A tabulated summary of the FDG PET literature. J Nucl Med. 2001; 42: 1S – 93S. | |
dc.identifier.citedreference | Heron DE, Andrade RS, Beriwal S, Smith RP. PET‐CT in radiation oncology: the impact on diagnosis, treatment planning, and assessment of treatment response. Am J Clin Oncol. 2008; 31: 352 – 362. | |
dc.identifier.citedreference | Zaidi H, Vees H, Wissmeyer M. Molecular PET/CT imaging‐guided radiation therapy treatment planning. Acad Radiol. 2009; 16: 1108 – 1133. | |
dc.identifier.citedreference | Nestle U, Weber W, Hentschel M, Grosu AL. Biological imaging in radiation therapy: role of positron emission tomography. Phys Med Biol. 2009; 54: R1 – R25. | |
dc.identifier.citedreference | Jan S, Santin G, Strul D, et al. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol. 2004; 49: 4543 – 4561. | |
dc.identifier.citedreference | Mac Manus MP, Hicks RJ. The role of positron emission tomography/computed tomography in radiation therapy planning for patients with lung cancer. Semin Nucl Med. 2012; 42: 308 – 319. | |
dc.identifier.citedreference | Mac Manus MP, Everitt S, Bayne M, et al. The use of fused PET/CT images for patient selection and radical radiotherapy target volume definition in patients with non‐small cell lung cancer: results of a prospective study with mature survival data. Radiother Oncol. 2013; 106: 292 – 298. | |
dc.identifier.citedreference | Gregoire V, Haustermans K, Geets X, Roels S, Lonneux M. PET‐based treatment planning in radiotherapy: a new standard? J Nucl Med. 2007; 48 ( Suppl 1 ): 68S – 77S. | |
dc.identifier.citedreference | Chua S, Dickson J, Groves AM. PET imaging for prediction of response to therapy and outcome in oesophageal carcinoma. Eur J Nucl Med Mol Imaging. 2011; 38: 1591 – 1594. | |
dc.identifier.citedreference | Cazaentre T, Morschhauser F, Vermandel M, et al. Pre‐therapy 18F‐FDG PET quantitative parameters help in predicting the response to radioimmunotherapy in non‐Hodgkin lymphoma. Eur J Nucl Med Mol Imaging. 2010; 37: 494 – 504. | |
dc.identifier.citedreference | Lee HY, Hyun SH, Lee KS, et al. Volume‐based parameter of 18)F‐FDG PET/CT in malignant pleural mesothelioma: prediction of therapeutic response and prognostic implications. Ann Surg Oncol. 2010; 17: 2787 – 2794. | |
dc.identifier.citedreference | El Naqa I, Grigsby P, Apte A, et al. Exploring feature‐based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009; 42: 1162 – 1171. | |
dc.identifier.citedreference | Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012; 102: 239 – 245. | |
dc.identifier.citedreference | Soret M, Bacharach SL, Buvat I. Partial‐volume effect in PET tumor imaging. J Nucl Med. 2007; 48: 932 – 945. | |
dc.identifier.citedreference | Alessio AM, Kinahan PE, Cheng PM, Vesselle H, Karp JS. PET/CT scanner instrumentation, challenges, and solutions. Radiol Clin North Am. 2004; 42: 1017 – 1032. | |
dc.identifier.citedreference | Lartizien C, Kinahan PE, Swensson R, et al. Evaluating image reconstruction methods for tumor detection in 3‐dimensional whole‐body PET oncology imaging. J Nucl Med. 2003; 44: 276 – 290. | |
dc.identifier.citedreference | Visvikis D, Griffiths D, Costa DC, Bomanji J, Ell PJ. Clinical evaluation of 2D versus 3D whole‐body PET image quality using a dedicated BGO PET scanner. Eur J Nucl Med Mol Imaging. 2005; 32: 1050 – 1056. | |
dc.identifier.citedreference | Mawlawi O, Pan T, Macapinlac HA. PET/CT imaging techniques, considerations, and artifacts. J Thorac Imaging. 2006; 21: 99 – 110. | |
dc.identifier.citedreference | Kirov AS, Schmidtlein CR, Kang H, Lee N. Rationale, instrumental accuracy, and challenges of PET quantification for tumor segmentation in radiation treatment planning. In: Hsieh C‐H ed. Positron Emission Tomography‐Current Clinical and Research Aspects. InTech, 2012. | |
dc.identifier.citedreference | Nestle U, Kremp S, Schaefer‐Schuler A, 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 – 1348. | |
dc.identifier.citedreference | Terezakis SA, Hunt MA, Kowalski A, et al. [ 18 F]FDG‐positron emission tomography coregistration with computed tomography scans for radiation treatment planning of lymphoma and hematologic malignancies. Int J Radiat Oncol Biol Phys. 2011; 81: 615 – 622. | |
dc.identifier.citedreference | Steenbakkers RJ, Duppen JC, Fitton I, 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 – 448. | |
dc.identifier.citedreference | Hofheinz F, Potzsch C, Oehme L, et al. Automatic volume delineation in oncological PET. Evaluation of a dedicated software tool and comparison with manual delineation in clinical data sets. Nuklearmedizin. 2012; 51: 9 – 16. | |
dc.identifier.citedreference | Boudraa AO, Zaidi H. Image segmentation techniques in nuclear medicine imaging. In: Zaidi H, ed. Quantitative Analysis in Nuclear Medicine Imaging. New York: Springer; 2006: 308 – 357. | |
dc.identifier.citedreference | Belhassen S, Zaidi H. A novel fuzzy C‐means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys. 2010; 37: 1309 – 1324. | |
dc.identifier.citedreference | Lee JA. Segmentation of positron emission tomography images: some recommendations for target delineation in radiation oncology. Radiother Oncol. 2010; 96: 302 – 307. | |
dc.identifier.citedreference | Schaefer A, Kremp S, Hellwig D, Rube C, Kirsch CM, 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 – 1999. | |
dc.identifier.citedreference | El Naqa I, Yang D, Apte A, et al. Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning. Med Phys. 2007; 34: 4738 – 4749. | |
dc.identifier.citedreference | Song Q, Bai J, Han D, et al. Optimal co‐segmentation of tumor in PET‐CT images with context information. IEEE Trans Med Imaging. 2013; 32: 1685 – 1697. | |
dc.identifier.citedreference | Bagci U, Udupa JK, Mendhiratta N, et al. Joint segmentation of anatomical and functional images: applications in quantification of lesions from PET, PET‐CT, MRI‐PET, and MRI‐PET‐CT images. Med Image Anal. 2013; 17: 929 – 945. | |
dc.identifier.citedreference | Hatt M, Boussion N, Cheze‐Le Rest C, Visvikis D, Pradier O. Metabolically active volumes automatic delineation methodologies in PET imaging: review and perspectives. Cancer Radiother. 2012; 16: 70 – 81. | |
dc.identifier.citedreference | Foster B, Bagci U, Mansoor A, Xu ZY, Mollura DJ. A review on segmentation of positron emission tomography images. Comput Biol Med. 2014; 50: 76 – 96. | |
dc.identifier.citedreference | Geets X, Lee JA, Bol A, Lonneux M, Gregoire V. A gradient‐based method for segmenting FDG‐PET images: methodology and validation. Eur J Nucl Med Mol Imaging. 2007; 34: 1427 – 1438. | |
dc.identifier.citedreference | De Bernardi E, Faggiano E, Zito F, Gerundini P, Baselli G. Lesion quantification in oncological positron emission tomography: a maximum likelihood partial volume correction strategy. Med Phys. 2009; 36: 3040 – 3049. | |
dc.identifier.citedreference | Iglesias JE, Sabuncu MR. Multi‐atlas segmentation of biomedical images: A survey. Med Image Anal. 2015; 24: 205 – 219. | |
dc.identifier.citedreference | Yu H, Caldwell C, Mah K, Mozeg D. Coregistered FDG PET/CT‐based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans Med Imaging. 2009; 28: 374 – 383. | |
dc.identifier.citedreference | Udupa JK, Leblanc VR, Zhuge Y, et al. A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph. 2006; 30: 75 – 87. | |
dc.identifier.citedreference | Tylski P, Stute S, Grotus N, et al. Comparative assessment of methods for estimating tumor volume and standardized uptake value in (18)F‐FDG PET. J Nucl Med. 2010; 51: 268 – 276. | |
dc.identifier.citedreference | Hatt M, Cheze Le Rest C, Albarghach N, Pradier O, Visvikis D. PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging. 2011; 38: 663 – 672. | |
dc.identifier.citedreference | Berthon B, Marshall C, Edwards A, Evans M, Spezi E. Influence of cold walls on PET image quantification and volume segmentation: a phantom study. Med Phys. 2013; 40: 082505. | |
dc.identifier.citedreference | Berthon B, Marshall C, Evans M, Spezi E. Evaluation of advanced automatic PET segmentation methods using nonspherical thin‐wall inserts. Med Phys. 2014; 41: 022502. | |
dc.identifier.citedreference | Biehl KJ, Kong FM, Dehdashti F, 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 – 1812. | |
dc.identifier.citedreference | Van Dalen JA, Hoffmann AL, Dicken V, et al. A novel iterative method for lesion delineation and volumetric quantification with FDG PET. Nucl Med Commun. 2007; 28: 485 – 493. | |
dc.identifier.citedreference | Erdi YE, Mawlawi O, Larson SM, et al. Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer. 1997; 80: 2505 – 2509. | |
dc.identifier.citedreference | Paulino AC, Johnstone PA. FDG‐PET in radiotherapy treatment planning: pandora’s box? Int J Radiat Oncol Biol Phys. 2004; 59: 4 – 5. | |
dc.identifier.citedreference | Biehl KJ, Kong F, Dehdashti F, et al. FDG‐PET definition of gross tumor volume for radiotherapy of non‐small‐cell lung cancer: is a single SUV threshold approach appropriate? J Nucl Med. 2006; 47: 1808 – 1812. | |
dc.identifier.citedreference | 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 – 114. | |
dc.identifier.citedreference | Nehmeh SA, El‐Zeftawy H, Greco C, et al. An iterative technique to segment PET lesions using a Monte Carlo based mathematical model. Med Phys. 2009; 36: 4803 – 4809. | |
dc.identifier.citedreference | Black QC, Grills IS, Kestin LL, et al. Defining a radiotherapy target with positron emission tomography. Int J Radiat Oncol Biol Phys. 2004; 60: 1272 – 1282. | |
dc.identifier.citedreference | 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. 2010; 37: 2165 – 2187. | |
dc.identifier.citedreference | Li H, Thorstad WL, Biehl KJ, et al. A novel PET tumor delineation method based on adaptive region‐growing and dual‐front active contours. Med Phys. 2008; 35: 3711 – 3721. | |
dc.identifier.citedreference | Wanet M, Lee JA, Weynand B, 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. Radiother Oncol. 2011; 98: 117 – 125. | |
dc.identifier.citedreference | Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell. 1994; 16: 641 – 647. | |
dc.identifier.citedreference | Pavlidis T, Liow YT. Integrating region growing and edge‐detection. IEEE Trans Pattern Anal Mach Intell. 1990; 12: 225 – 233. | |
dc.identifier.citedreference | Ibanez L, Schroeder W, Ng L, Cates J. The ITK Software Guide. Clifton Park, NY: Kitware Inc.; 2017. | |
dc.identifier.citedreference | Day E, Betler J, Parda D, et al. A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients. Med Phys. 2009; 36: 4349 – 4358. | |
dc.identifier.citedreference | Hofheinz F, Langner J, Petr J, et al. An automatic method for accurate volume delineation of heterogeneous tumors in PET. Med Phys. 2013; 40: 082503. | |
dc.identifier.citedreference | Pieczynski W. Modèles de Markov en traitement d’images. Trait Signal. 2003; 20: 255 – 278. | |
dc.identifier.citedreference | Delignon Y, Marzouki A, Pieczynski W. Estimation of generalized mixtures and its application in image segmentation. IEEE Trans Image Process. 1997; 6: 1364 – 1375. | |
dc.identifier.citedreference | Montgomery DW, Amira A, Zaidi H. Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model. Med Phys. 2007; 34: 722 – 736. | |
dc.identifier.citedreference | 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 – 4235. | |
dc.identifier.citedreference | Caillol H, Pieczynski W, Hillion A. Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation. IEEE Trans Image Process. 1997; 6: 425 – 440. | |
dc.identifier.citedreference | Salzenstein F, Collet C, Lecam S, Hatt M. Non‐stationary fuzzy Markov chain. Pattern Recogn Lett. 2007; 28: 2201 – 2208. | |
dc.identifier.citedreference | 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 – 893. | |
dc.identifier.citedreference | Hatt M, Lamare F, Boussion N, et al. Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET. Phys Med Biol. 2007; 52: 3467 – 3491. | |
dc.identifier.citedreference | Hatt M, Cheze le Rest C, Descourt P, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010; 77: 301 – 308. | |
dc.identifier.citedreference | Duda RO, Hart PE, Stork DG. Pattern Classification, 2nd edn. New York: Wiley; 2001. | |
dc.identifier.citedreference | Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv. 1999; 31: 264 – 323. | |
dc.identifier.citedreference | Berthon B, Marshall C, Evans M, Spezi E. ATLAAS: an automatic decision tree‐based learning algorithm for advanced image segmentation in positron emission tomography. Phys Med Biol. 2016; 61: 4855 – 4869. | |
dc.identifier.citedreference | LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521: 436 – 444. | |
dc.identifier.citedreference | Avendi MR, Kheradvar A, Jafarkhani H. A combined deep‐learning and deformable‐model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal. 2016; 30: 108 – 119. | |
dc.identifier.citedreference | Cha KH, Hadjiiski L, Samala RK, Chan HP, Caoili EM, Cohan RH. Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets. Med Phys. 2016; 43: 1882. | |
dc.identifier.citedreference | Kerhet A, Small C, Quon H, et al. Application of machine learning methodology for PET‐based definition of lung cancer. Curr Oncol. 2010; 17: 41 – 47. | |
dc.identifier.citedreference | Lian C, Ruan S, Denoeux T, Jardin F, Vera P. Selecting radiomic features from FDG‐PET images for cancer treatment outcome prediction. Med Image Anal. 2016; 32: 257 – 268. | |
dc.identifier.citedreference | Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present.. any future? Eur J Nucl Med Mol Imaging. 2017; 44: 151 – 165. | |
dc.identifier.citedreference | Materka A, Strzelecki M. Texture analysis methods – A review. In: COST B11. Brussels: Technical University of Lodz, Institute of Electronics. 1998. | |
dc.identifier.citedreference | Jain AK, Farrokhnia F. Unsupervised texture segmentation using Gabor filters. Pattern Recogn. 1991; 24: 1167 – 1186. | |
dc.identifier.citedreference | Arivazhagan S, Ganesan L. Texture classification using wavelet transform. Pattern Recogn Lett. 2003; 24: 1513 – 1521. | |
dc.identifier.citedreference | Stachowiak GP, Podsiadlo P, Stachowiak GW. A comparison of texture feature extraction methods for machine condition monitoring and failure analysis. Tribol Lett. 2005; 20: 133 – 147. | |
dc.identifier.citedreference | Haralick RM, Shanmugam K. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973; 3: 610 – 621. | |
dc.identifier.citedreference | Amadasun M, King R. Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern. 1989; 19: 1264 – 1274. | |
dc.identifier.citedreference | Galloway M. Texture analysis using grey level run lengths. Comput Vision Graph. 1975; 4: 172 – 179. | |
dc.identifier.citedreference | Mohamed S, Youssef A, El‐Saadany E, Salama MM. Artificial life feature selection techniques for prostrate cancer diagnosis using TRUS images. In: International Conference Image Analysis and Recognition. 2005: 903 – 913. | |
dc.identifier.citedreference | Woods BJ, Clymer BD, Kurc T, et al. Malignant‐lesion segmentation using 4D co‐occurrence texture analysis applied to dynamic contrast‐enhanced magnetic resonance breast image data. J Magn Reson Imaging. 2007; 25: 495 – 501. | |
dc.identifier.citedreference | McNitt‐Gray MF, Har EM, Wyckoff N, Sayre JW, Goldin JG, Aberle DR. A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med Phys. 1999; 26: 880 – 888. | |
dc.identifier.citedreference | Silva AC, Paiva AC, Carvalho PCP, Gattass M. Semivariogram and SGLDM methods comparison for the diagnosis of solitary lung nodule. Pattern Recognition and Image Analysis. Pt 2, Proceedings 3523, 2005; 479 – 486. | |
dc.identifier.citedreference | Uppaluri R, Hoffman EA, Sonka M, Hartley PG, Hunninghake GW, McLennan G. Computer recognition of regional lung disease patterns. Am J Resp Crit Care. 1999; 160: 648 – 654. | |
dc.identifier.citedreference | Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, Thelen M. Automatic detection and quantification of ground‐glass opacities on high‐resolution CT using multiple neural networks: comparison with a density mask. Am J Roentgenol. 2000; 175: 1329 – 1334. | |
dc.identifier.citedreference | Chabat F, Yang GZ, Hansell DM. Obstructive lung diseases: texture classification for differentiation at CT. Radiology. 2003; 228: 871 – 877. | |
dc.identifier.citedreference | Ganeshan B, Abaleke S, Young RCD, Chatwin CR, Miles KA. Texture analysis of non‐small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 2010; 10: 137 – 143. | |
dc.identifier.citedreference | Pichler BJ, Kolb A, Nagele T, Schlemmer HP. PET/MRI: paving the way for the next generation of clinical multimodality imaging applications. J Nucl Med. 2010; 51: 333 – 336. | |
dc.identifier.citedreference | Zaidi H, Del Guerra A. An outlook on future design of hybrid PET/MRI systems. Med Phys. 2011; 38: 5667 – 5689. | |
dc.identifier.citedreference | Yu H, Caldwell C, Mah K, et al. Automated radiation targeting in head‐and‐neck cancer using region‐based texture analysis of PET and CT images. Int J Radiat Oncol Biol Phys. 2009; 75: 618 – 625. | |
dc.identifier.citedreference | Markel D, Caldwell C, Alasti H, et al. Automatic segmentation of lung carcinoma using 3D texture features in 18‐FDG PET/CT. Int J Mol Imaging. 2013; 2013: 980769. | |
dc.identifier.citedreference | Schaefer A, Vermandel M, Baillet C, et al. Impact of consensus contours from multiple PET segmentation methods on the accuracy of functional volume delineation. Eur J Nucl Med Mol Imaging. 2016; 43: 911 – 924. | |
dc.identifier.citedreference | Shepherd T, Teras M, Beichel RR, et al. Comparative study with new accuracy metrics for target volume contouring in PET image guided radiation therapy. IEEE Trans Med Imaging. 2012; 31: 2006 – 2024. | |
dc.identifier.citedreference | Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004; 23: 903 – 921. | |
dc.identifier.citedreference | McGurk RJ, Bowsher J, Lee JA, Das SK. Combining multiple FDG‐PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods. Med Phys. 2013; 40: 042501. | |
dc.identifier.citedreference | Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng. 2000; 2: 315 – 337. | |
dc.identifier.citedreference | Bettinardi V, Castiglioni I, De Bernardi E, Gilardi MC. PET quantification: strategies for partial volume correction. Clin Transl Imaging. 2014; 2: 199 – 218. | |
dc.identifier.citedreference | Lucy LB. An iterative technique for the rectification of observed distributions. Astron J. 1974; 79: 745. | |
dc.identifier.citedreference | Richardson WH. Bayesian‐based iterative method of image restoration. J Opt Soc Am. 1972; 62: 55. | |
dc.identifier.citedreference | Kirov AS, Piao JZ, Schmidtlein CR. Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology. Phys Med Biol. 2008; 53: 2577 – 2591. | |
dc.identifier.citedreference | Boussion N, Cheze Le Rest C, Hatt M, Visvikis D. Incorporation of wavelet‐based denoising in iterative deconvolution for partial volume correction in whole‐body PET imaging. Eur J Nucl Med Mol Imaging. 2009; 36: 1064 – 1075. | |
dc.identifier.citedreference | Barbee DL, Flynn RT, Holden JE, Nickles RJ, Jeraj R. A method for partial volume correction of PET‐imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function. Phys Med Biol. 2010; 55: 221 – 236. | |
dc.identifier.citedreference | Alessio AM, Stearns CW, Tong S, et al. Application and evaluation of a measured spatially variant system model for PET image reconstruction. IEEE Trans Med Imaging. 2010; 29: 938 – 949. | |
dc.identifier.citedreference | Jakoby BW, Bercier Y, Watson CC, Bendriem B, Townsend DW. Performance characteristics of a New LSO PET/CT scanner with extended axial field‐of‐view and PSF reconstruction. IEEE Trans Nucl Sci. 2009; 56: 633 – 639. | |
dc.identifier.citedreference | De Bernardi E, Mazzoli M, Zito F, Baselli G. Resolution recovery in PET during AWOSEM reconstruction: a performance evaluation study. IEEE Trans Nucl Sci. 2007; 54: 1626 – 1638. | |
dc.identifier.citedreference | Teo BK, Seo Y, Bacharach SL, et al. Partial‐volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data. J Nucl Med. 2007; 48: 802 – 810. | |
dc.identifier.citedreference | Boussion N, Hatt M, Visvikis D. Partial volume correction in PET based on functional volumes. J Nucl Med. 2008; 49: 388P. | |
dc.identifier.citedreference | Chen CH, Muzic RF Jr, Nelson AD, Adler LP. Simultaneous recovery of size and radioactivity concentration of small spheroids with PET data. J Nucl Med. 1999; 40: 118 – 130. | |
dc.identifier.citedreference | De Bernardi E, Soffientini C, Zito F, Baselli G. Joint Segmentation and Quantification of Oncological Lesions in PET/CT: Preliminary Evaluation on a Zeolite Phantom. Anaheim, California: IEEE NSS MIC 2012, October 29 ‐ November 3, 2012, 2012; 3306 – 3310. | |
dc.identifier.citedreference | King AD. Multimodality imaging of head and neck cancer. Cancer Imaging. Spec No A, 2007; 7: S37 – S46. | |
dc.identifier.citedreference | Munley MT, Marks LB, Scarfone C, et al. Multimodality nuclear medicine imaging in three‐dimensional radiation treatment planning for lung cancer: challenges and prospects. Lung Cancer. 1999; 23: 105 – 114. | |
dc.identifier.citedreference | Chen R, Parry JJ, Akers WJ, et al. Multimodality imaging of gene transfer with a receptor‐based reporter gene. J Nucl Med. 2010; 51: 1456 – 1463. | |
dc.identifier.citedreference | DeFeo EM, Wu C‐L, McDougal WS, Cheng LL. A decade in prostate cancer: from NMR to metabolomics. Nat Rev Urol. 2011; 8: 301 – 311. | |
dc.identifier.citedreference | Hsu AR, Cai W, Veeravagu A, et al. Multimodality molecular imaging of glioblastoma growth inhibition with vasculature‐targeting fusion toxin VEGF121/rGel. J Nucl Med. 2007; 48: 445 – 454. | |
dc.identifier.citedreference | Smith WL, Lewis C, Bauman G, et al. Prostate volume contouring: a 3D analysis of segmentation using 3DTRUS, CT, and MR. Int J Radiat Oncol Biol Phys. 2007; 67: 1238 – 1247. | |
dc.identifier.citedreference | Buijsen J, Van den Bogaard J, Van der Weide H, et al. FDG‐PET‐CT reduces the interobserver variability in rectal tumor delineation. Radiother Oncol. 2012; 102: 371 – 376. | |
dc.identifier.citedreference | Van Baardwijk A, Bosmans G, Boersma L, 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 – 778. | |
dc.identifier.citedreference | Metwally H, Courbon F, David I, et al. Coregistration of prechemotherapy PET‐CT for planning pediatric Hodgkin’s disease radiotherapy significantly diminishes interobserver variability of clinical target volume definition. Int J Radiat Oncol Biol Phys. 2011; 80: 793 – 799. | |
dc.identifier.citedreference | Anderson CM, Sun W, Buatti JM, et al. Interobserver and intermodality variability in GTV delineation on simulation CT, FDG‐PET, and MR images of head and neck cancer. Jacobs J Radiat Oncol. 2014; 1: 006. | |
dc.identifier.citedreference | Zheng Y, Sun X, Wang J, Zhang L, Di X, Xu Y. FDG‐PET/CT imaging for tumor staging and definition of tumor volumes in radiation treatment planning in non‐small cell lung cancer. Oncology letters. 2014; 7: 1015 – 1020. | |
dc.identifier.citedreference | Sebbahi A, Herment A, De Cesare A, Mousseaux E. Multimodality cardiovascular image segmentation using a deformable contour model. Comput Med Imag Grap. 1997; 21: 79 – 89. | |
dc.identifier.citedreference | Zheng J, El Naqa I, Rowold FE, et al. Quantitative assessment of coronary artery plaque vulnerability by high‐resolution magnetic resonance imaging and computational biomechanics: a pilot study ex vivo. Magn Reson Med. 2005; 54: 1360 – 1368. | |
dc.identifier.citedreference | El Naqa I. Radiotherapy informatics: targeted control. Enterp Imaging Ther Radiol Manag. 2008; 18: 39 – 42. | |
dc.identifier.citedreference | Yang D, Zheng J, Nofal A, Wu Y, Deasy J, El Naqa I. Techniques and software tool for 3D multimodality medical image segmentation. J Radiat Oncol Inform. 2009; 1: 1 – 21. | |
dc.identifier.citedreference | Chan TF, Sandberg BY, Vese LA. Active Contours without Edges for Vector‐Valued Images. J Vis Commun Image Represent. 2000; 11: 130 – 141. | |
dc.identifier.citedreference | Shah J. Curve evolution and segmentation functionals: application to color images. Int Conf Image Process Proc. 1996; 1: 461 – 464. | |
dc.identifier.citedreference | Cui H, Wang X, Zhou J, et al. Topology polymorphism graph for lung tumor segmentation in PET‐CT images. Phys Med Biol. 2015; 60: 4893 – 4914. | |
dc.identifier.citedreference | Werner‐Wasik M, Nelson AD, Choi W, et al. What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient‐based method using a NSCLC digital PET phantom. Int J Radiat Oncol Biol Phys. 2012; 82: 1164 – 1171. | |
dc.identifier.citedreference | Fogh SE, Farach A, Intenzo C, et al. Pathologic correlation of PET‐CT based auto contouring for radiation planning in lung cancer. Int J Radiat Oncol Biol Phys. 2010; 78: S202 – S203. | |
dc.identifier.citedreference | Daisne JF, Sibomana M, Bol A, Doumont T, Lonneux M, Gregoire V. Tri‐dimensional automatic segmentation of PET volumes based on measured source‐to‐background ratios: influence of reconstruction algorithms. Radiother Oncol. 2003; 69: 247 – 250. | |
dc.identifier.citedreference | Sebastian TB, Manjeshwar RM, Akhurst TJ, Miller JV. Objective PET lesion segmentation using a spherical mean shift algorithm. Lect Notes Comput Sc. 2006; 4191: 782 – 789. | |
dc.identifier.citedreference | Zaidi H, Abdoli M, Fuentes CL, El Naqa IM. Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma. Eur J Nucl Med Mol Imaging. 2012; 39: 881 – 891. | |
dc.identifier.citedreference | Dewalle‐Vignion AS, Betrouni N, Lopes R, Huglo D, Stute S, Vermandel M. A new method for volume segmentation of PET images, based on possibility theory. IEEE Trans Med Imaging. 2011; 30: 409 – 423. | |
dc.identifier.citedreference | Abdoli M, Dierckx RA, Zaidi H. Contourlet‐based active contour model for PET image segmentation. Med Phys. 2013; 40: 082507. | |
dc.identifier.citedreference | Daisne JF, Duprez T, Weynand B, 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. | |
dc.identifier.citedreference | Hatt M, Cheze‐le Rest C, Van Baardwijk A, Lambin P, Pradier O, Visvikis D. Impact of tumor size and tracer uptake heterogeneity in (18)F‐FDG PET and CT non‐small cell lung cancer tumor delineation. J Nucl Med. 2011; 52: 1690 – 1697. | |
dc.identifier.citedreference | Hatt M, Maitre AL, Wallach D, Fayad H, Visvikis D. Comparison of different methods of incorporating respiratory motion for lung cancer tumor volume delineation on PET images: a simulation study. Phys Med Biol. 2012; 57: 7409 – 7430. | |
dc.identifier.citedreference | Berthon B, Marshall C, Evans M, Spezi E. Implementation and optimization of automatic 18F‐FDG PET segmentation methods. Eur J Nucl Med Mol Imaging. 2013; 39 ( Suppl 2 ): S385. | |
dc.identifier.citedreference | Ollers M, Bosmans G, Van Baardwijk A, et al. The integration of PET‐CT scans from different hospitals into radiotherapy treatment planning. Radiother Oncol. 2008; 87: 142 – 146. | |
dc.identifier.citedreference | Knausl B, Hirtl A, Dobrozemsky G, et al. PET based volume segmentation with emphasis on the iterative TrueX algorithm. Z Med Phys. 2012; 22: 29 – 39. | |
dc.identifier.citedreference | Schaefer A, Nestle U, Kremp S, et al. Multi‐centre calibration of an adaptive thresholding method for PET‐based delineation of tumour volumes in radiotherapy planning of lung cancer. Nuklearmed‐Nucl Med. 2012; 51: 101 – 110. | |
dc.identifier.citedreference | Mackie TR, Gregoire V. International Commission on Radiation Units and Measurements (ICRU) Report 83. Prescribing, Recording, and Reporting Photon‐Beam Intensity‐Modulated Radiation Therapy (IMRT). Vol. 10(1) 2010. | |
dc.identifier.citedreference | Fischer BM, Olsen MWB, Ley CD, et al. How few cancer cells can be detected by positron emission tomography? A frequent question addressed by an in vitro study. Eur J Nucl Med Mol Imaging. 2006; 33: 697 – 702. | |
dc.identifier.citedreference | Berthon B, Spezi E, Galavis P, et al. Towards a standard for the evaluation of PET Auto‐Segmentation methods: requirements and implementation. Med Phys. 2017, accepted for publication. | |
dc.identifier.citedreference | Janssen MH, Aerts HJ, Ollers MC, 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 – 465. | |
dc.identifier.citedreference | Shepherd T, Owenius R. Gaussian process models of dynamic PET for Functional Volume Definition In Radiation Oncology. IEEE Trans Med Imaging. 2012; 31: 1542 – 1556. | |
dc.identifier.citedreference | Lelandais B, Ruan S, Denoeux T, Vera P, Gardin I. Fusion of multi‐tracer PET images for dose painting. Med Image Anal. 2014; 18: 1247 – 1259. | |
dc.identifier.citedreference | NEMA NU 2‐2001. Performance Measurements of Positron Emission Tomographs. Rosslyn, VA: National Electrical Manufacturers Association; 2001. | |
dc.identifier.citedreference | Hunt DC, Easton H, Caldwell CB. Design and construction of a quality control phantom for SPECT and PET imaging. Med Phys. 2009; 36: 5404 – 5411. | |
dc.identifier.citedreference | DiFilippo FP, Price JP, Kelsch DN, Muzic RF Jr. Porous phantoms for PET and SPECT performance evaluation and quality assurance. Med Phys. 2004; 31: 1183 – 1194. | |
dc.identifier.citedreference | Zito F, De Bernardi E, Soffientini C, et al. The use of zeolites to generate PET phantoms for the validation of quantification strategies in oncology. Med Phys. 2012; 39: 5353 – 5361. | |
dc.identifier.citedreference | Larsson SA, Jonsson C, Pagani M, Johansson L, Jacobsson H. A novel phantom design for emission tomography enabling scatter‐ and attenuation‐”free” single‐photon emission tomography imaging. Eur J Nucl Med. 2000; 27: 131 – 139. | |
dc.identifier.citedreference | El‐Ali H, Ljungberg M, Strand SE, Palmer J, Malmgren L, Nilsson J. Calibration of a radioactive ink‐based stack phantom and its applications in nuclear medicine. Cancer Biother Radiopharm. 2003; 18: 201 – 207. | |
dc.identifier.citedreference | Miller M, Hutchins G. 3D Anatomically accurate phantoms for PET and SPECT imaging. J Nucl Med. 2008; 49: 65P. | |
dc.identifier.citedreference | Kirov AS, Sculley E, Schmidtlein CR, et al. A new phantom allowing realistic non‐uniform activity distributions for PET quantification, abstract presented at the 2011 joint AAPM/COMP meeting. Med Phys. 2011; 38: 3387. | |
dc.identifier.citedreference | Zaidi H, Xu XG. Computational anthropomorphic models of the human anatomy: The path to realistic Monte Carlo modeling in medical imaging. Annu Rev Biomed Eng. 2007; 9: 471 – 500. | |
dc.identifier.citedreference | Wang W, Georgi JC, Nehmeh SA, et al. Evaluation of a compartmental model for estimating tumor hypoxia via FMISO dynamic PET imaging. Phys Med Biol. 2009; 54: 3083 – 3099. | |
dc.identifier.citedreference | Berthon B, Häggström I, Apte A, et al. PETSTEP: generation of synthetic PET lesions for fast evaluation of segmentation methods. Med Phys. 2015; 31: 969 – 980. | |
dc.identifier.citedreference | Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982; 1: 113 – 122. | |
dc.identifier.citedreference | Asma E, Ahn S, Ross SG, Chen A, Manjeshwar RM. Accurate and consistent lesion quantitation with clinically acceptable penalized likelihood images. Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2012 IEEE,. 4062‐4066 2012. | |
dc.identifier.citedreference | Segars WP, Sturgeon G, Mendonca S, Grimes J, Tsui BM. 4D XCAT phantom for multimodality imaging research. Med Phys. 2010; 37: 4902 – 4915. | |
dc.identifier.citedreference | Zubal IG, Harrell CR, Smith EO, Rattner Z, Gindi G, Hoffer PB. Computerized three‐dimensional segmented human anatomy. Med Phys. 1994; 21: 299 – 302. | |
dc.identifier.citedreference | McLennan A, Reilhac A, Brady M. SORTEO: Monte Carlo‐based simulator with list‐mode capabilities. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009; 3751 – 3754 | |
dc.identifier.citedreference | Jan S, Benoit D, Becheva E, et al. GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy. Phys Med Biol. 2011; 56: 881 – 901. | |
dc.identifier.citedreference | Harrison R, Gillispie S, Schmitz R, Lewellen T. Modeling block detectors in SimSET. J Nucl Med. 2008; 49; 410. | |
dc.identifier.citedreference | Lamare F, Turzo A, Bizais Y, Le Rest CC, Visvikis D. Validation of a Monte Carlo simulation of the philips allegro/GEMINI PET systems using GATE. Phys Med Biol. 2006; 51: 943 – 962. | |
dc.identifier.citedreference | Bayne M, Hicks RJ, Everitt S, et al. Reproducibility of “intelligent” contouring of gross tumor volume in non‐small‐cell lung cancer on PET/CT images using a standardized visual method. Int J Radiat Oncol Biol Phys. 2010; 77: 1151 – 1157. | |
dc.identifier.citedreference | Kirov AS, Fanchon L. Pathology‐validated PET image data sets and their role for PET segmentation. Clin Trans Imaging. 2014; 2: 253 – 267. | |
dc.identifier.citedreference | Fogh SE, Kannarkatt J, Farach A, et al. Pathologic correlation of PET‐CT based auto contouring for radiation treatment planning in lung cancer. J Thorac Oncol. 2009; 4: S528 – S529. | |
dc.identifier.citedreference | van Loon J, Siedschlag C, Stroom J, et al. Microscopic disease extension in three dimensions for non‐small‐cell lung cancer: development of a prediction model using pathology‐validated positron emission tomography and computed tomography features. Int J Radiat Oncol Biol Phys. 2012; 82: 448 – 456. | |
dc.identifier.citedreference | Axente M, He J, Bass CP, et al. An alternative approach to histopathological validation of PET imaging for radiation therapy image‐guidance: a proof of concept. Radiother Oncol. 2014; 110: 309 – 316. | |
dc.identifier.citedreference | Fanchon LM, Dogan S, Moreira AL, et al. Feasibility of in situ, high‐resolution correlation of tracer uptake with histopathology by quantitative autoradiography of biopsy specimens obtained under 18F‐FDG PET/CT guidance. J Nucl Med. 2015; 56: 538 – 544. | |
dc.identifier.citedreference | Dubuisson M‐P, Jain AK. A modified Hausdorff distance for object matching. Pattern Recognition, 1994. Vol. 1‐Conference A: Computer Vision #x0026; Image Processing., Proceedings of the 12th IAPR International Conference on, 1994; 1: 566 – 568. | |
dc.identifier.citedreference | Kim H, Monroe JI, Lo S, et al. Quantitative evaluation of image segmentation incorporating medical consideration functions. Med Phys. 2015; 42: 3013 – 3023. | |
dc.identifier.citedreference | Gregoire V, Jeraj R, Lee JA, O’Sullivan B. Radiotherapy for head and neck tumours in 2012 and beyond: conformal, tailored, and adaptive? Lancet Oncol. 2012; 13: e292 – e300. | |
dc.identifier.citedreference | Skretting A, Evensen JF, Londalen AM, Bogsrud TV, Glomset OK, Eilertsen K. A gel tumour phantom for assessment of the accuracy of manual and automatic delineation of gross tumour volume from FDG‐PET/CT. Acta Oncol. 2013; 52: 636 – 644. | |
dc.identifier.citedreference | David S, Visvikis D, Roux C, Hatt M. Multi‐observation PET image analysis for patient follow‐up quantitation and therapy assessment. Phys Med Biol. 2011; 56: 5771 – 5788. | |
dc.identifier.citedreference | David S, Visvikis D, Quellec G, et al. Image change detection using paradoxical theory for patient follow‐up quantitation and therapy assessment. IEEE Trans Med Imaging. 2012; 31: 1743 – 1753. | |
dc.identifier.citedreference | Lelandais B, Gardin I, Mouchard L, Vera P, Ruan S. Segmentation of biological target volumes on multi‐tracer PET images based on information fusion for achieving dose painting in radiotherapy. Med Image Comput Comput Assist Interv ‐MICCAI. 2012; 15: 545 – 552. | |
dc.identifier.citedreference | Frings V, De Langen AJ, Smit EF, et al. Repeatability of metabolically active volume measurements with 18F‐FDG and 18F‐FLT PET in non‐small cell lung cancer. J Nucl Med. 2010; 51: 1870 – 1877. | |
dc.identifier.citedreference | Hatt M, Cheze‐Le Rest C, Aboagye EO, et al. Reproducibility of 18F‐FDG and 3′‐deoxy‐3′‐18F‐fluorothymidine PET tumor volume measurements. J Nucl Med. 2010; 51: 1368 – 1376. | |
dc.identifier.citedreference | Boellaard R, Delgado‐Bolton R, Oyen WJ, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015; 42: 328 – 354. | |
dc.identifier.citedreference | Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving Considerations for PET response criteria in solid tumors. J Nucl Med. 2009; 50: 122s – 150s. | |
dc.identifier.citedreference | MacFarlane CR, American College of Radiologists. ACR accreditation of nuclear medicine and PET imaging departments”. J Nucl Med Technol. 2006; 34: 18 – 24. | |
dc.identifier.citedreference | Barrett HH. Objective assessment of image quality: effects of quantum noise and object variability. J Opt Soc Am A. 1990; 7: 1266 – 1278. | |
dc.identifier.citedreference | Barrett HH, Abbey CK, Clarkson E. Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood‐generating functions. J Opt Soc Am A. 1998; 15: 1520 – 1535. | |
dc.identifier.citedreference | Barrett HH, Denny JL, Wagner RF, Myers KJ. Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance. J Opt Soc Am A. 1995; 12: 834 – 852. | |
dc.identifier.citedreference | Barrett HH, Kupinski MA, Mueller S, Halpern HJ, Morris JC, Dwyer R. Objective assessment of image quality VI: imaging in radiation therapy. Phys Med Biol. 2013; 58: 8197 – 8213. | |
dc.identifier.citedreference | Fessler JA, Rogers WL. Spatial resolution properties of penalized‐likelihood image reconstruction: space‐invariant tomographs. IEEE T Image Process. 1996; 5: 1346 – 1358. | |
dc.identifier.citedreference | Barrett HH, Wilson DW, Tsui BMW. Noise properties of the EM algorithm. 1. Theory. Phys Med Biol. 1994; 39: 833 – 846. | |
dc.identifier.citedreference | Markel D, Zaidi H, El Naqa I. Novel multimodality segmentation using level sets and Jensen‐Rényi divergence. Med Phys. 2013; 40: 121908. | |
dc.identifier.citedreference | Fessler JA, Ficaro EP, Clinthorne NH, Lange K. Grouped‐coordinate ascent algorithms for penalized‐likelihood transmission image reconstruction. IEEE Trans Med Imaging. 1997; 16: 166 – 175. | |
dc.identifier.citedreference | Krol A, Li S, Shen L, Xu Y. Preconditioned alternating projection algorithms for maximum a posteriori ECT reconstruction. Inverse Prob. 2012; 28: 115005. | |
dc.identifier.citedreference | Rapisarda E, Presotto L, De Bernardi E, Gilardi MC, Bettinardi V. Optimized Bayes variational regularization prior for 3D PET images. Comput Med Imag Grap. 2014; 38: 445 – 457. | |
dc.identifier.citedreference | Ahn S, Ross SG, Asma E, et al. Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET. Phys Med Biol. 2015; 60: 5733 – 5751. | |
dc.identifier.citedreference | Arens AI, Troost EG, Hoeben BA, et al. Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome. Eur J Nucl Med Mol Imaging. 2014; 41: 915 – 924. | |
dc.identifier.citedreference | Henriques de Figueiredo B, Zacharatou C, Galland‐Girodet S, et al. Hypoxia imaging with [18F]‐FMISO‐PET for guided dose escalation with intensity‐modulated radiotherapy in head‐and‐neck cancers. Strahlenther Onkol. 2015; 191: 217 – 224. | |
dc.identifier.citedreference | Low DA, Nystrom M, Kalinin E, et al. A method for the reconstruction of four‐dimensional synchronized CT scans acquired during free breathing. Med Phys. 2003; 30: 1254 – 1263. | |
dc.identifier.citedreference | Wink N, Panknin C, Solberg TD. Phase versus amplitude sorting of 4D‐CT data. J Appl Clin Med Phys. 2006; 7: 77 – 85. | |
dc.identifier.citedreference | Olsen JR, Lu W, Hubenschmidt JP, et al. Effect of novel amplitude/phase binning algorithm on commercial four‐dimensional computed tomography quality. Int J Radiat Oncol Biol Phys. 2008; 70: 243 – 252. | |
dc.identifier.citedreference | Nehmeh SA, Erdi YE, Rosenzweig KE, et al. Reduction of respiratory motion artifacts in PET imaging of lung cancer by respiratory correlated dynamic PET: methodology and comparison with respiratory gated PET. J Nucl Med. 2003; 44: 1644 – 1648. | |
dc.identifier.citedreference | Qiao F, Pan T, Clark Jr JW, Mawlawi OR. A motion‐incorporated reconstruction method for gated PET studies. Phys Med Biol. 2006; 51: 3769. | |
dc.identifier.citedreference | Pai‐Chun Melinda C, Osama M, Sadek AN, et al. Design of respiration averaged CT for attenuation correction of the PET data from PET/CT. Med Phys. 2007; 34: 2039 – 2047. | |
dc.identifier.citedreference | Berlinger K, Sauer O, Vences L, Roth M. A simple method for labeling CT images with respiratory states. Med Phys. 2006; 33: 3144 – 3148. | |
dc.identifier.citedreference | Qiao F, Pan T, Clark JJW, Mawlawi O. Joint model of motion and anatomy for PET image reconstruction. Med Phys. 2007; 34: 4626 – 4639. | |
dc.identifier.citedreference | Dawood M, Buther F, Lang N, Schober O, Schafers KP. Respiratory gating in positron emission tomography: a quantitative comparison of different gating schemes. Med Phys. 2007; 34: 3067 – 3076. | |
dc.identifier.citedreference | Bruyant PP, Rest CCL, Turzo A, Jarritt P, Carson K, Visvikis D. A method for synchronizing an external respiratory signal with a list‐mode PET acquisition. Med Phys. 2007; 34: 4472 – 4475. | |
dc.identifier.citedreference | Nehmeh SA, Erdi YE, Meirelles GS, et al. Deep‐inspiration breath‐hold PET/CT of the thorax. J Nucl Med. 2007; 48: 22 – 26. | |
dc.identifier.citedreference | Sureshbabu W, Mawlawi O. PET/CT imaging artifacts. J Nucl Med Technol. 2005; 33: 156 – 161; quiz 163‐154. | |
dc.identifier.citedreference | Chang G, Chang T, Pan T, Clark JW Jr, Mawlawi OR. Implementation of an automated respiratory amplitude gating technique for PET/CT: clinical evaluation. J Nuc Med. 2010; 51: 16 – 24. | |
dc.identifier.citedreference | Buther F, Ernst I, Dawood M, et al. Detection of respiratory tumour motion using intrinsic list mode‐driven gating in positron emission tomography. Eur J Nucl Med Mol Imaging. 2010; 37: 2315 – 2327. | |
dc.identifier.citedreference | Schleyer PJ, O’Doherty MJ, Barrington SF, Marsden PK. Retrospective data‐driven respiratory gating for PET/CT. Phys Med Biol. 2009; 54: 1935 – 1950. | |
dc.identifier.citedreference | Kesner AL, Kuntner C. A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. Med Phys. 2010; 37: 5550 – 5559. | |
dc.identifier.citedreference | 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 – 3600. | |
dc.identifier.citedreference | Yalavarthy PK, Low D, Noel C, et al. Current role of PET in oncology: Potentials and challenges in the management of non‐small cell lung cancer. 2008 42nd Asilomar Conference on Signals, Systems and Computers, 1067‐1071. 2008. | |
dc.identifier.citedreference | Buther F, Vehren T, Schafers KP, Schafers M. Impact of data‐driven respiratory gating in clinical PET. Radiology. 2016; 281: 229 – 238. | |
dc.identifier.citedreference | Kesner AL, Chung JH, Lind KE, et al. Validation of software gating: a practical technology for respiratory motion correction in PET. Radiology. 2016; 281: 152105. | |
dc.identifier.citedreference | Kesner AL, Schleyer PJ, Buther F, Walter MA, Schafers KP, Koo PJ. On transcending the impasse of respiratory motion correction applications in routine clinical imaging – a consideration of a fully automated data driven motion control framework. EJNMMI Physics. 2014; 1: 8. | |
dc.identifier.citedreference | Aristophanous M, Yap JT, Killoran JH, Chen AB, Berbeco RI. Four‐dimensional positron emission tomography: implications for dose painting of high‐uptake regions. Int J Radiat Oncol Biol Phys. 2011; 80: 900 – 908. | |
dc.identifier.citedreference | Aristophanous M, Berbeco RI, Killoran JH, et al. Clinical utility of 4D FDG‐PET/CT scans in radiation treatment planning. Int J Radiat Oncol Biol Phys. 2012; 82: e99 – e105. | |
dc.identifier.citedreference | Lamb JM, Robinson C, Bradley J, et al. Generating lung tumor internal target volumes from 4D‐PET maximum intensity projections. Med Phys. 2011; 38: 5732 – 5737. | |
dc.identifier.citedreference | Guerra L, Meregalli S, Zorz A, et al. Comparative evaluation of CT‐based and respiratory‐gated PET/CT‐based planning target volume (PTV) in the definition of radiation treatment planning in lung cancer: preliminary results. Eur J Nucl Med Mol Imaging. 2014; 41: 702 – 710. | |
dc.identifier.citedreference | Chirindel A, Adebahr S, Schuster D, et al. Impact of 4D‐(18)FDG‐PET/CT imaging on target volume delineation in SBRT patients with central versus peripheral lung tumors. Multi‐reader comparative study. Radiother Oncol. 2015; 115: 335 – 341. | |
dc.identifier.citedreference | Pierce LA, Elston BF, Clunie DA, Nelson D, Kinahan PE. A Digital Reference Object to Analyze Calculation Accuracy of PET Standardized Uptake Value. Radiology. 2015; 277: 538 – 545. | |
dc.identifier.citedreference | Withofs N, Bernard C, Van der Rest C, et al. FDG PET/CT for rectal carcinoma radiotherapy treatment planning: comparison of functional volume delineation algorithms and clinical challenges. J Appl Clin Med Phys. 2014; 15: 4696. | |
dc.identifier.citedreference | Shepherd T, Berthon B, Galavis P, et al. Design of a benchmark platform for evaluating PET‐based contouring accuracy in oncology applications. Eur J Nucl Med Mol Imaging. 2012; 39: S264. | |
dc.identifier.citedreference | Berthon B, Spezi E, Schmidtlein CR, et al. Development of a software platform for evaluating automatic PET segmentation methods. Radiother Oncol. 2013; 111: S166. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.