The Pivotal Role of FDG-PET/CT in Modern Medicine, Academic Radiology, vol.21, issue.2, pp.232-249, 2014. ,
DOI : 10.1016/j.acra.2013.11.002
The Role of Imaging in Radiation Therapy Planning: Past, Present, and Future, BioMed Research International, vol.38, issue.2, p.231090, 2014. ,
DOI : 10.1038/sj.onc.1206681
)F-FDG-PET/CT 643 in evaluating response to therapy in solid tumors: where we are and where we can go, J Nucl, vol.644, issue.18 ,
State-Of-The-Art and Recent Advances in Quantification for Therapeutic 646 Follow-Up in Oncology Using PET, Front. Med, vol.2, p.18, 2015. ,
Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology, Nuclear Medicine and Molecular Imaging, vol.40, issue.Suppl 1, pp.1-15, 2014. ,
DOI : 10.1007/s13139-013-0260-2
Role in Therapy Response, Resistance, and Clinical Outcome, p.652 ,
An Update on Novel Quantitative Techniques in the Context of Evolving Whole-Body PET Imaging, PET Clinics, vol.10, issue.1, pp.45-655, 2015. ,
DOI : 10.1016/j.cpet.2014.09.004
PET functional volume delineation: a robustness and repeatability study, European Journal of Nuclear Medicine and Molecular Imaging, vol.97, issue.12, pp.663-72, 2011. ,
DOI : 10.1007/s00259-010-1688-6
URL : https://hal.archives-ouvertes.fr/inserm-00574273
EANM procedure guidelines for tumour PET imaging: version 1.0, p.664 ,
M??thodologies de d??finition automatique des volumes m??taboliquement actifs en TEP??: ??valuation et perspectives, Cancer/Radioth??rapie, vol.16, issue.1, pp.70-81, 2012. ,
DOI : 10.1016/j.canrad.2011.07.243
A review on segmentation of positron emission tomography images, Computers in Biology and Medicine, vol.50, pp.76-96, 2014. ,
DOI : 10.1016/j.compbiomed.2014.04.014
Global and local methods of unsupervised Bayesian 671 segmentations of images, Mach. Graph. Vis, pp.39-52, 1993. ,
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.8, pp.1367-78, 2007. ,
DOI : 10.1109/TPAMI.2007.1059
URL : https://hal.archives-ouvertes.fr/hal-01347974
Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET, Physics in Medicine and Biology, vol.52, issue.12, pp.3467-91, 2007. ,
DOI : 10.1088/0031-9155/52/12/010
URL : https://hal.archives-ouvertes.fr/inserm-00150348
A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET, IEEE Transactions on Medical Imaging, vol.28, issue.6, pp.881-679, 2009. ,
DOI : 10.1109/TMI.2008.2012036
URL : https://hal.archives-ouvertes.fr/inserm-00372910
Accurate automatic delineation of heterogeneous functional volumes in 682 positron emission tomography for oncology applications, Int J Radiat Oncol Biol Phys, vol.77, issue.1, pp.683-301, 2010. ,
Impact of Tumor Size and Tracer Uptake Heterogeneity in 18F-FDG PET and CT Non-Small Cell Lung Cancer Tumor Delineation, Journal of Nuclear Medicine, vol.52, issue.11, pp.1690-1697, 2011. ,
DOI : 10.2967/jnumed.111.092767
URL : https://hal.archives-ouvertes.fr/hal-00703670
Reproducibility of 18F-FDG and 3'-deoxy-3'- 689 18F-fluorothymidine PET tumor volume measurements, J Nucl Med, vol.51, issue.9, pp.1368-76, 2010. ,
Use of FDG-PET to guide dose prescription heterogeneity in stereotactic body 692 radiation therapy for lung cancers with volumetric modulated arc therapy: a feasibility study, p.693 ,
Hypoxia 696 imaging with [18F]-FMISO-PET for guided dose escalation with intensity-modulated radiotherapy 697 in head-and-neck cancers, Strahlenther. Onkol. Organ Dtsch. Rontgengesellschaft Al, 2014. ,
Semiautomatic methods for 700 segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and 701 neck carcinomas and their relation to clinical outcome, Eur. J. Nucl. Med. Mol. Imaging, vol.41, issue.5, pp.702-915, 2014. ,
Comparison of different methods of incorporating respiratory motion for lung cancer tumor volume delineation on PET images: a simulation study, Physics in Medicine and Biology, vol.57, issue.22, pp.7409-7439, 2012. ,
DOI : 10.1088/0031-9155/57/22/7409
URL : https://hal.archives-ouvertes.fr/hal-00749010
Impact of the accuracy of 707 automatic tumour functional volume delineation on radiotherapy treatment planning, p.708 ,
Fuzzy clustering with a fuzzy covariance matrix, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, 1978. ,
DOI : 10.1109/CDC.1978.268028
Control 17th Symp, Adapt. Process, pp.761-766, 1978. ,
Generalized fuzzy c-means clustering strategies using L/sub p/ norm distances, IEEE Transactions on Fuzzy Systems, vol.8, issue.5, pp.576-582, 2000. ,
DOI : 10.1109/91.873580
Robust image segmentation using FCM with spatial constraints based on 714 new kernel-induced distance measure, IEEE Trans. Syst. Man Cybern. Part B Cybern, vol.34, issue.4, pp.715-1907, 2004. ,
Unsupervised possibilistic clustering, Pattern Recognition, vol.39, issue.1, pp.5-21, 2006. ,
DOI : 10.1016/j.patcog.2005.07.005
Belief Functions: A Revision of Plausibility Conflict and Pignistic Conflict, SUM, pp.190-203, 2013. ,
DOI : 10.1007/978-3-642-40381-1_15
A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Transactions on Medical Imaging, vol.21, issue.3, pp.193-199, 2002. ,
DOI : 10.1109/42.996338
A Robust Fuzzy Local Information C-Means Clustering Algorithm, IEEE Transactions on Image Processing, vol.19, issue.5, p.724 ,
DOI : 10.1109/TIP.2010.2040763
Unsupervised optimal fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.7, pp.773-780, 1989. ,
DOI : 10.1109/34.192473
How to assess background activity: introducing a histogram-based analysis as a 729 first step for accurate one-step PET quantification, Nucl. Med. Commun, vol.35, issue.3, pp.316-324, 2014. ,
Influence of cold walls on PET image quantification and volume segmentation: A phantom study, Medical Physics, vol.34, issue.10, pp.82505-734, 2013. ,
DOI : 10.1118/1.2432404
Effects of cold sphere walls in PET phantom measurements on the volume reproducing threshold, Physics in Medicine and Biology, vol.55, issue.4, pp.1099-737, 2010. ,
DOI : 10.1088/0031-9155/55/4/013
Simultaneous truth and performance level estimation 739 (STAPLE): an algorithm for the validation of image segmentation, IEEE Trans Med Imaging, vol.23, issue.7, pp.740-903, 2004. ,
Comparative methods for PET image 742 segmentation in pharyngolaryngeal squamous cell carcinoma, Eur. J. Nucl. Med. Mol. Imaging, vol.743, issue.5, pp.39-881, 2012. ,
Incorporating Patient-Specific Variability in the Simulation of Realistic Whole-Body 18F-FDG 746 Distributions for Oncology Applications, Proc. IEEE 9, pp.2026-2038, 2009. ,
Investigation of realistic PET simulations incorporating tumor 749 patient's specificity using anthropomorphic models: creation of an oncology database, p.750 ,
Development and Application of the New Dynamic NURBS-based Cardiac-Torso 752 (NCAT) phantom, 2001. ,
Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE, Physics in Medicine and Biology, vol.51, issue.4, pp.943-62, 2006. ,
DOI : 10.1088/0031-9155/51/4/013
Characterisation of SUV accuracy in FDG PET using 3-D RAMLA and the Philips Allegro PET 758 scanner, J. Nucl. Med, vol.757, issue.103, 2004. ,
RapidArc, intensity modulated photon and proton techniques for recurrent 761 prostate cancer in previously irradiated patients: a treatment planning comparison study, Radiat Oncol, vol.760, issue.4, pp.762-796, 2009. ,
A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET, Medical Physics, vol.69, issue.3, pp.1309-1333, 2010. ,
DOI : 10.1118/1.3301610
Automated and robust PERCIST-based thresholding 766 framework for whole body PET-CT studies, Proc. Annu. Int. Conf, pp.5335-5338, 2012. ,
Segmentation of PET images for computer-aided functional quantification of tuberculosis in 770 small animal models, IEEE Trans. Biomed. Eng, vol.769, issue.3, pp.61-711, 2014. ,
Design of a benchmark platform 773 for evaluating PET-based contouring accuracy in oncology applications, Eur. J. Nucl. Med. Mol. 774 Imaging, vol.39, pp.264-264, 2012. ,