A. Anderson, A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion, Mathematical Medicine and Biology, vol.22, issue.2, pp.163-186, 2005.
DOI : 10.1093/imammb/dqi005

E. Angelini, O. Clatz, E. Mandonnet, E. Konukoglu, L. Capelle et al., Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications, Current Medical Imaging Reviews, vol.3, issue.4, pp.262-276, 2007.
DOI : 10.2174/157340507782446241
URL : https://hal.archives-ouvertes.fr/inria-00616021

E. D. Angelini, J. Delon, L. Capelle, and E. Mandonnet, Differential MRI analysis for quantification of low grade glioma growth, Medical Image Analysis, vol.16, issue.1, pp.114-126, 2012.
DOI : 10.1016/j.media.2011.05.014

B. Bach-cuadra, C. Pollo, A. Bardera, O. Cuisenaire, and J. P. Thiran, Atlas-based segmentation of pathological brain MR images using a model of lesion growth, IEEE TMI, vol.23, pp.1301-1315, 2004.

C. Baillard, P. Hellier, and C. Barillot, Segmentation of brain 3D MR images using level sets and dense registration, Medical Image Analysis, vol.5, issue.3, pp.185-194, 2001.
DOI : 10.1016/S1361-8415(01)00039-1
URL : https://hal.archives-ouvertes.fr/inria-00536389

L. Bangiyev, E. M. Rossi, R. Young, T. Shepherd, E. Knopp et al., Adult Brain Tumor Imaging: State of the Art, Seminars in Roentgenology, p.39, 2014.
DOI : 10.1053/j.ro.2013.11.001

S. Bauer, L. Nolte, R. , and M. , Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.2018-2021, 2011.
DOI : 10.1109/ISBI.2011.5872808

E. Bearer, J. Lowengrub, H. Frieboes, Y. Chuang, F. Jin et al., Multiparameter Computational Modeling of Tumor Invasion, Cancer Research, vol.69, issue.10, p.694493, 2009.
DOI : 10.1158/0008-5472.CAN-08-3834

R. Benjamin, C. Thierry, and S. Santiago, A multiscale mathematical model of cancer, and its use in analyzing irradiation therapies, Theoretical Biology and Medical Modelling, 2006.

G. Bertrand and G. Malandain, A new characterization of three-dimensional simple points, Pattern Recognition Letters, vol.15, issue.2, pp.169-175, 1994.
DOI : 10.1016/0167-8655(94)90046-9
URL : https://hal.archives-ouvertes.fr/inria-00615050

L. Bohman, K. R. Swanson, J. L. Moore, R. Rockne, C. Mandigo et al., Magnetic Resonance Imaging Characteristics of Glioblastoma Multiforme: Implications for Understanding Glioma Ontogeny, Neurosurgery, vol.67, issue.5, p.671319, 2010.
DOI : 10.1227/NEU.0b013e3181f556ab

P. Bondiau, E. Konukoglu, O. Clatz, H. Delingette, M. Frenay et al., Biocomputing: Numerical simulation of glioblastoma growth and comparison with conventional irradiation margins, Physica Medica, vol.27, issue.2, pp.103-108, 2011.
DOI : 10.1016/j.ejmp.2010.05.002

P. K. Burgess, P. M. Kulesa, J. D. Murray, A. Jr, and E. C. , The Interaction of Growth Rates and Diffusion Coefficients in a Three-dimensional Mathematical Model of Gliomas, Journal of Neuropathology and Experimental Neurology, vol.56, issue.6, pp.56704-713, 1997.
DOI : 10.1097/00005072-199706000-00008

A. Buzdar and R. Freedman, M.D. Anderson Cancer Care Series, 2007.

M. Bynevelt, J. Britton, H. Seymour, E. Macsweeney, N. Thomas et al., FLAIR imaging in the follow-up of low-grade gliomas: time to dispense with the dual-echo?, Neuroradiology, vol.43, issue.2, pp.129-133, 2001.
DOI : 10.1007/s002340000389

S. Chakrabarty and F. Hanson, Optimal control of drug delivery to brain tumors for a distributed parameters model, Proceedings of the 2005, American Control Conference, 2005., 2005.
DOI : 10.1109/ACC.2005.1470086

O. Clatz, M. Sermesant, P. Bondiau, H. Delingette, S. K. Warfield et al., Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation, IEEE Transactions on Medical Imaging, vol.24, issue.10, pp.1334-1346, 2005.
DOI : 10.1109/TMI.2005.857217

D. Cobzas, P. Mosayebi, A. Murtha, J. , and M. , Tumor Invasion Margin on the Riemannian Space of Brain Fibers, pp.531-539, 2009.
DOI : 10.1007/978-3-642-04271-3_65

T. Colin, A. Iollo, and D. Lombardi, SYSTEM IDENTIFICATION IN TUMOR GROWTH MODELING USING SEMI-EMPIRICAL EIGENFUNCTIONS, Mathematical Models and Methods in Applied Sciences, vol.22, issue.06, p.221250003, 2012.
DOI : 10.1142/S0218202512500030

D. L. Collins, P. Neelin, T. M. Peters, and A. C. Evans, Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space, Journal of Computer Assisted Tomography, vol.18, issue.2, pp.192-205, 1994.
DOI : 10.1097/00004728-199403000-00005

D. Corwin, C. Holdsworth, R. C. Rockne, A. D. Trister, M. M. Mrugala et al., Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma, PLoS ONE, vol.14, issue.11, p.79115, 2013.
DOI : 10.1371/journal.pone.0079115.t002

G. Cruywagen, D. Woodward, P. Tracqui, G. Bartoo, J. Murray et al., THE MODELLING OF DIFFUSIVE TUMOURS, Journal of Biological Systems, vol.03, issue.04, pp.937-982, 1995.
DOI : 10.1142/S0218339095000836

N. Danchaivijitr, A. D. Waldman, D. J. Tozer, C. E. Benton, G. B. Caseiras et al., Low-Grade Gliomas: Do Changes in rCBV Measurements at Longitudinal Perfusion-weighted MR Imaging Predict Malignant Transformation?, Radiology, vol.247, issue.1, pp.170-178, 2008.
DOI : 10.1148/radiol.2471062089

L. Deangelis, Brain Tumors, New England Journal of Medicine, vol.344, issue.2, pp.114-123, 2001.
DOI : 10.1056/NEJM200101113440207

T. Deisboeck, Z. Wang, P. Macklin, C. , and V. , Multiscale cancer modeling, Annu. Rev. Biomed. Eng, vol.13, p.12755, 2011.

T. S. Deisboeck, L. Zhang, J. Yoon, C. , and J. , In silico cancer modeling: is it ready for prime time?, Nature Clinical Practice Oncology, vol.293, issue.1, pp.34-42, 2008.
DOI : 10.1038/ncponc1237

S. Drabycz, G. Roldán, P. D. Robles, D. Adler, J. Mcintyre et al., An analysis of image texture, tumor location, and REFERENCES MGMT promoter methylation in glioblastoma using magnetic resonance imaging, Neuroimage, issue.2, pp.491398-1405, 2010.

U. Ebert and W. Van-saarloos, Front propagation into unstable states: universal algebraic convergence towards uniformly translating pulled fronts, Physica D: Nonlinear Phenomena, vol.146, issue.1-4, pp.1-99, 2000.
DOI : 10.1016/S0167-2789(00)00068-3

E. A. Eisenhauer, P. Therasse, J. Bogaerts, L. H. Schwartz, D. Sargent et al., New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1), European Journal of Cancer, vol.45, issue.2, pp.228-247, 2009.
DOI : 10.1016/j.ejca.2008.10.026

A. Fedorov, R. Beichel, J. Kalpathy-cramer, J. Finet, J. Fillion-robin et al., 3D Slicer as an image computing platform for the Quantitative Imaging Network, Magnetic Resonance Imaging, vol.30, issue.9, 2012.
DOI : 10.1016/j.mri.2012.05.001

F. Jr, S. Martins, M. Vilela, and M. , A growth model for primary cancer. Physica A: Statistical Mechanics and its Applications, pp.569-580, 1998.

F. Jr, S. Martins, M. Vilela, and M. , A growth model for primary cancer (II). New rules, progress curves and morphology transitions, Physica A: Statistical Mechanics and its Applications, pp.245-256, 1999.
DOI : 10.1016/S0378-4371(99)00301-5

V. Fonov, A. Evans, R. Mckinstry, C. Almli, C. et al., Unbiased nonlinear average age-appropriate brain templates from birth to adulthood, NeuroImage, vol.47, pp.102-102, 2009.
DOI : 10.1016/S1053-8119(09)70884-5

V. Fonov, A. C. Evans, K. Botteron, C. R. Almli, R. C. Mckinstry et al., Unbiased average age-appropriate atlases for pediatric studies, NeuroImage, vol.54, issue.1, pp.313-327, 2011.
DOI : 10.1016/j.neuroimage.2010.07.033

H. Frieboes, J. Lowengrub, S. Wise, X. Zheng, P. Macklin et al., Computer simulation of glioma growth and morphology, NeuroImage, vol.37, pp.59-70, 2007.
DOI : 10.1016/j.neuroimage.2007.03.008

E. Galanis, J. C. Buckner, M. J. Maurer, R. Sykora, R. Castillo et al., Validation of neuroradiologic response assessment in gliomas: Measurement by RECIST, two-dimensional, computer-assisted tumor area, and computer-assisted tumor volume methods, Neuro-Oncology, vol.8, issue.2, pp.156-165, 2006.
DOI : 10.1215/15228517-2005-005

E. Geremia, B. H. Menze, M. Prastawa, M. Weber, A. Criminisi et al., Brain tumor cell density estimation from multi-modal MR REFERENCES images based on a synthetic tumor growth model, Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging, pp.273-282, 2013.

C. Gerin, J. Pallud, C. Deroulers, P. Varlet, C. Oppenheim et al., Quantitative characterization of the imaging limits of diffuse low-grade oligodendrogliomas, Neuro-Oncology, vol.15, issue.10, 2013.
DOI : 10.1093/neuonc/not072

J. Gevertz, G. Gillies, and S. Torquato, Simulating tumor growth in confined heterogeneous environments, Physical Biology, vol.5, issue.3, p.36010, 2008.
DOI : 10.1088/1478-3975/5/3/036010

A. Giese, L. Kluwe, B. Laube, H. Meissner, M. Berens et al., Migration of Human Glioma Cells on Myelin, Neurosurgery, vol.38, issue.4, pp.755-764, 1996.
DOI : 10.1227/00006123-199604000-00026

M. L. Goodenberger and R. B. Jenkins, Genetics of adult glioma, Cancer Genetics, vol.205, issue.12, 2012.
DOI : 10.1016/j.cancergen.2012.10.009

A. Gooya, G. Biros, D. , and C. , Deformable Registration of Glioma Images Using EM Algorithm and Diffusion Reaction Modeling, IEEE Transactions on Medical Imaging, vol.30, issue.2, pp.375-390, 2011.
DOI : 10.1109/TMI.2010.2078833

A. Gooya, K. Pohl, M. Bilello, G. Biros, D. et al., Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model, Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.532-540, 2011.
DOI : 10.1109/42.790458

A. Gooya, K. M. Pohl, M. Bilello, L. Cirillo, G. Biros et al., GLISTR: Glioma Image Segmentation and Registration, IEEE Transactions on Medical Imaging, vol.31, issue.10, pp.311941-1954, 2012.
DOI : 10.1109/TMI.2012.2210558

H. Harpold, E. Alvord, and J. Swanson, The Evolution of Mathematical Modeling of Glioma Proliferation and Invasion, Journal of Neuropathology and Experimental Neurology, vol.66, issue.1, p.1, 2007.
DOI : 10.1097/nen.0b013e31802d9000

J. Henson, S. Ulmer, H. , and G. , Brain Tumor Imaging in Clinical Trials, American Journal of Neuroradiology, vol.29, issue.3, pp.419-424, 2008.
DOI : 10.3174/ajnr.A0963

C. Hogea, C. Davatzikos, and G. Biros, An image-driven parameter estimation problem for a reaction???diffusion glioma growth model with mass effects, Journal of Mathematical Biology, vol.10, issue.3, pp.793-825, 2008.
DOI : 10.1007/s00285-007-0139-x

C. S. Hogea, B. T. Murray, and J. A. Sethian, Simulating complex tumor dynamics from avascular to vascular growth using a general level-set method, Journal of Mathematical Biology, vol.67, issue.1, pp.86-134, 2006.
DOI : 10.1007/s00285-006-0378-2

C. J. Holmes, R. Hoge, L. Collins, R. Woods, A. W. Toga et al., Enhancement of MR Images Using Registration for Signal Averaging, Journal of Computer Assisted Tomography, vol.22, issue.2, pp.324-333, 1998.
DOI : 10.1097/00004728-199803000-00032

T. Jaermann, G. Crelier, K. Pruessmann, X. Golay, T. Netsch et al., SENSE-DTI at 3 T. Magnetic resonance in medicine, pp.230-236, 2004.

S. Jbabdi, E. Mandonnet, H. Duffau, L. Capelle, K. Swanson et al., Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging, Magnetic Resonance in Medicine, vol.6, issue.3, pp.616-624, 2005.
DOI : 10.1002/mrm.20625

M. Jenkinson, P. Bannister, M. Brady, and S. Smith, Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images, NeuroImage, vol.17, issue.2, pp.825-841, 2002.
DOI : 10.1006/nimg.2002.1132

M. Jenkinson and S. Smith, A global optimisation method for robust affine registration of brain images, Medical Image Analysis, vol.5, issue.2, pp.143-156, 2001.
DOI : 10.1016/S1361-8415(01)00036-6

Y. Jiang, J. Pjesivac-grbovic, C. Cantrell, and J. P. Freyer, A Multiscale Model for Avascular Tumor Growth, Biophysical Journal, vol.89, issue.6, pp.3884-3894, 2005.
DOI : 10.1529/biophysj.105.060640

E. Kaal and C. Vecht, The management of brain edema in brain tumors, Current Opinion in Oncology, vol.16, issue.6, p.593, 2004.
DOI : 10.1097/01.cco.0000142076.52721.b3

A. Kansal, S. Torquato, E. Chiocca, and T. Deisboeck, Emergence of a Subpopulation in a Computational Model of Tumor Growth, Journal of Theoretical Biology, vol.207, issue.3, pp.431-441, 2000.
DOI : 10.1006/jtbi.2000.2186

F. O. Kaster, B. H. Menze, M. Weber, and F. A. Hamprecht, Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations, Proc MICCAI Workshop on Medical Computer Vision, 2010.
DOI : 10.1007/978-3-642-18421-5_8
URL : https://hal.archives-ouvertes.fr/hal-00813770

J. Keener and J. Sneyd, Mathematical physiology, 1998.
DOI : 10.1007/978-0-387-75847-3

B. M. Kelm, B. H. Menze, O. Nix, C. M. Zechmann, and F. A. Hamprecht, Estimating Kinetic Parameter Maps From Dynamic Contrast-Enhanced MRI Using Spatial Prior Knowledge, IEEE Transactions on Medical Imaging, vol.28, issue.10, pp.1534-1581, 2009.
DOI : 10.1109/TMI.2009.2019957

R. Kikinis, M. E. Shenton, D. V. Iosifescu, R. W. Mccarley, P. Saiviroonporn et al., A digital brain atlas for surgical planning, model-driven segmentation, and teaching. Visualization and Computer Graphics, IEEE Transactions on, vol.2, issue.3, pp.232-241, 1996.

E. Konukoglu, Modeling Glioma Growth and Personalizing Growth Models in Medical Images, 2009.
URL : https://hal.archives-ouvertes.fr/tel-00633697

E. Konukoglu, O. Clatz, P. Bondiau, H. Delingette, and N. Ayache, Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins, Medical Image Analysis, vol.14, issue.2, pp.111-125, 2010.
DOI : 10.1016/j.media.2009.11.005
URL : https://hal.archives-ouvertes.fr/inria-00616107

E. Konukoglu, O. Clatz, P. Bondiau, M. Sermesant, H. Delingette et al., Towards an Identification of Tumor Growth Parameters from Time Series of Images, Medical Image Computing and Computer-Assisted Intervention, pp.549-556, 2007.
DOI : 10.1007/978-3-540-75757-3_67
URL : https://hal.archives-ouvertes.fr/inria-00616046

E. Konukoglu, O. Clatz, B. Menze, B. Stieltjes, M. Weber et al., Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations, IEEE Transactions on Medical Imaging, vol.29, issue.1, pp.77-95, 2009.
DOI : 10.1109/TMI.2009.2026413
URL : https://hal.archives-ouvertes.fr/inria-00616100

E. Konukoglu, O. Clatz, B. H. Menze, M. Weber, B. Stieltjes et al., Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations, IEEE Transactions on Medical Imaging, vol.29, issue.1, pp.77-95, 2010.
DOI : 10.1109/TMI.2009.2026413
URL : https://hal.archives-ouvertes.fr/inria-00616100

E. Konukoglu, M. Sermesant, O. Clatz, J. Peyrat, H. Delingette et al., A Recursive Anisotropic Fast Marching Approach to Reaction Diffusion Equation: Application to Tumor Growth Modeling, 2007.
DOI : 10.1007/978-3-540-73273-0_57
URL : https://hal.archives-ouvertes.fr/inria-00616056

S. K. Kyriacou, C. Davatzikos, S. J. Zinreich, and R. N. Bryan, Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI], IEEE Transactions on Medical Imaging, vol.18, issue.7, pp.580-592, 1999.
DOI : 10.1109/42.790458

J. C. Lagarias, J. A. Reeds, M. H. Wright, W. , and P. E. , Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions, SIAM Journal on Optimization, vol.9, issue.1, pp.112-147, 1998.
DOI : 10.1137/S1052623496303470

F. Laigle-donadey, N. Martin-duverneuil, J. Lejeune, E. Criniere, L. Capelle et al., Correlations between molecular profile and radiologic pattern in oligodendroglial tumors, Neurology, vol.63, issue.12, p.632360, 2004.
DOI : 10.1212/01.WNL.0000148642.26985.68

J. L. Lancaster, M. G. Woldorff, L. M. Parsons, M. Liotti, C. S. Freitas et al., Automated Talairach Atlas labels for functional brain mapping, Human Brain Mapping, vol.5, issue.3, pp.120-131, 2000.
DOI : 10.1002/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8

G. Langs, Y. Tie, L. Rigolo, A. J. Golby, G. et al., Localization of language areas in brain tumor patients by functional geometry alignment, Proc MICCAI Workshop on Computational Imaging Biomarkers for Tumors, p.8, 2010.

D. A. Lim, S. Cha, M. C. Mayo, M. Chen, E. Keles et al., Relationship of glioblastoma multiforme to neural stem cell regions predicts invasive and multifocal tumor phenotype, Neuro-Oncology, vol.9, issue.4, pp.424-429, 2007.
DOI : 10.1215/15228517-2007-023

Y. Liu, S. M. Sadowski, A. B. Weisbrod, E. Kebebew, R. M. Summers et al., Patient specific tumor growth prediction using multimodal images, Medical Image Analysis, vol.18, issue.3, pp.555-566, 2014.
DOI : 10.1016/j.media.2014.02.005
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3992298

M. Lorenzi, Deformation based morphometry of the brain for the development of surrogate markers in Alzheimer's disease, 2012.

S. Lu, D. Ahn, G. Johnson, C. , and S. , Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors, Am J Neuroradiology, vol.24, pp.937-978, 2003.

A. N. Mamelak and D. B. Jacoby, Targeted delivery of antitumoral therapy to glioma and other malignancies with synthetic chlorotoxin (TM-601), Expert Opinion on Drug Delivery, vol.46, issue.2, pp.175-186, 2007.
DOI : 10.1017/S1740925X06000044

E. Mandonnet, Mathematical modeling of glioma on MRI, Revue Neurologique, vol.167, issue.10, pp.715-720, 2011.
DOI : 10.1016/j.neurol.2011.07.009

E. Mandonnet, L. Capelle, and H. Duffau, Extension of paralimbic low grade gliomas: toward an anatomical classification based on white matter invasion patterns, Journal of Neuro-Oncology, vol.12, issue.2, pp.179-185, 2006.
DOI : 10.1007/s11060-005-9084-y
URL : https://hal.archives-ouvertes.fr/inserm-00147499

E. Mandonnet, J. Delattre, M. Tanguy, K. Swanson, A. Carpentier et al., Continuous growth of mean tumor diameter in a subset of grade II gliomas, Annals of Neurology, vol.54, issue.4, pp.524-528, 2003.
DOI : 10.1002/ana.10528

E. Mandonnet, S. Wait, L. Choi, and C. Teo, The importance of measuring the velocity of diameter expansion on MRI in upfront management of suspected WHO grade II glioma???????Case report, Neurochirurgie, vol.59, issue.2, 2013.
DOI : 10.1016/j.neuchi.2013.02.005

E. Mandonnet, P. A. Winkler, and H. Duffau, Direct electrical stimulation as an input gate into brain functional networks: principles, advantages and limitations, Acta Neurochirurgica, vol.91, issue.Pt 1, pp.185-193, 2010.
DOI : 10.1007/s00701-009-0469-0

J. Mazziotta, A. Toga, A. Evans, P. Fox, J. Lancaster et al., A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM), Philosophical Transactions of the Royal Society B: Biological Sciences, vol.356, issue.1412, pp.3561293-1322, 1412.
DOI : 10.1098/rstb.2001.0915

P. Mccorquodale, P. Colella, and H. Johansen, A Cartesian Grid Embedded Boundary Method for the Heat Equation on Irregular Domains, Journal of Computational Physics, vol.173, issue.2, pp.620-635, 2001.
DOI : 10.1006/jcph.2001.6900

B. H. Menze, M. P. Lichy, P. Bachert, B. M. Kelm, H. Schlemmer et al., Optimal classification of long echo timein vivo magnetic resonance spectra in the detection of recurrent brain tumors, NMR in Biomedicine, vol.50, issue.5, pp.599-610, 2006.
DOI : 10.1002/nbm.1041

B. H. Menze, E. Stretton, E. Konukoglu, and N. Ayache, Imagebased modeling of tumor growth in patients with glioma, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00825866

B. H. Menze, K. Van-leemput, A. Honkela, E. Konukoglu, M. Weber et al., A Generative Approach for Image-Based Modeling of Tumor Growth, IPMI, pp.735-747, 2011.
DOI : 10.1007/978-3-642-22092-0_60
URL : https://hal.archives-ouvertes.fr/hal-00813801

B. H. Menze, K. Van-leemput, D. Lashkari, M. Weber, N. Ayache et al., A generative model for brain tumor segmentation in multimodal images, Proc MICCAI, pp.151-159, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00813776

A. Miller, R. Alston, C. , and J. , VARIATION WITH AGE IN THE VOLUMES OF GREY AND WHITE MATTER IN THE CEREBRAL HEMISPHERES OF MAN: MEASUREMENTS WITH AN IMAGE ANALYSER, Neuropathology and Applied Neurobiology, vol.36, issue.2, pp.119-132, 1980.
DOI : 10.1016/0022-510X(70)90063-8

B. A. Moffat, T. L. Chenevert, C. R. Meyer, P. E. Mckeever, D. E. Hall et al., The Functional Diffusion Map: An Imaging Biomarker for the Early Prediction of Cancer Treatment Outcome, Neoplasia, vol.8, issue.4, p.259, 2006.
DOI : 10.1593/neo.05844

A. Mohamed and C. Davatzikos, Finite Element Modeling of Brain Tumor Mass-Effect from 3D Medical Images, Proc MICCAI, 2005.
DOI : 10.1007/11566465_50

A. Mohamed, E. I. Zacharakib, D. Shena, D. , and C. , Deformable registration of brain tumor images via a statistical model of tumor-induced deformation, Medical Image Analysis, vol.10, issue.5, pp.752-763, 2006.
DOI : 10.1016/j.media.2006.06.005

J. Murray, Mathematical biology, 2002.

M. L. Neal, A. D. Trister, T. Cloke, R. Sodt, S. Ahn et al., Discriminating Survival Outcomes in Patients with Glioblastoma Using a Simulation-Based, Patient-Specific Response Metric, PLoS ONE, vol.20, issue.1, p.51951, 2013.
DOI : 10.1371/journal.pone.0051951.s001

J. A. Nelder and R. Mead, A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-313, 1965.
DOI : 10.1093/comjnl/7.4.308

J. Pallud, P. Varlet, B. Devaux, S. Geha, M. Badoual et al., Diffuse lowgrade oligodendrogliomas extend beyond MRI-defined abnormalities, Neurology, issue.21, pp.741724-1731, 2010.
DOI : 10.1212/wnl.0b013e3181e04264

. Philips, Philips ambient lighting MR opens up a whole new world in patient care, CS_AmbientExp_versaille_E_final_july.pdf, 2010.

M. Powell, UOBYQA: unconstrained optimization by quadratic approximation, Mathematical Programming, pp.555-582, 2002.
DOI : 10.1007/s101070100290

M. Powell, The BOBYQA algorithm for bound constrained optimization without derivatives, 2009.

M. Prastawa, E. Bullitt, and G. Gerig, Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation, Proc MICCAI, 2005.
DOI : 10.1007/11566465_4

M. Prastawa, E. Bullitt, and G. Gerig, Simulation of brain tumors in MR images for evaluation of segmentation efficacy, Medical Image Analysis, vol.13, issue.2, 2009.
DOI : 10.1016/j.media.2008.11.002

S. Price, R. Jena, N. Burnet, P. Hutchinson, A. Dean et al., Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an imageguided biopsy study, pp.1969-1974, 2006.

S. J. Price, R. Jena, N. G. Burnet, T. A. Carpenter, J. D. Pickard et al., Predicting patterns of glioma recurrence using diffusion tensor imaging, European Radiology, vol.75, issue.9, pp.171675-1684, 2007.
DOI : 10.1007/s00330-006-0561-2

C. Pudney, Distance-Ordered Homotopic Thinning: A Skeletonization Algorithm for 3D Digital Images, Computer Vision and Image Understanding, vol.72, issue.3, pp.404-413, 1998.
DOI : 10.1006/cviu.1998.0680

J. Rees, H. Watt, H. R. Jäger, C. Benton, D. Tozer et al., Volumes and growth rates of untreated adult low-grade gliomas indicate risk of early malignant transformation, European Journal of Radiology, vol.72, issue.1, pp.54-64, 2009.
DOI : 10.1016/j.ejrad.2008.06.013

I. Rekik, S. Allassonnì-ere, O. Clatz, E. Geremia, E. Stretton et al., Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas, Computer Vision and Image Understanding, vol.117, issue.3, pp.238-249, 2012.
DOI : 10.1016/j.cviu.2012.11.001
URL : https://hal.archives-ouvertes.fr/hal-00813881

J. Rexilius, H. Hahn, M. Schluter, S. Kohle, H. Bourquain et al., A Framework for the Generation of Realistic Brain Tumor Phantoms and Applications, Proc MICCAI, 2004.
DOI : 10.1007/978-3-540-30136-3_31

B. Bernard, A. Idbaih, D. Psimaras, and L. Dainese, A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy, Clinical Cancer Research, vol.18, issue.18, pp.5071-5080, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00744626

T. Riklin-raviv, B. H. Menze, K. Van-leemput, B. Stieltjes, M. A. Weber et al., Joint segmentation via patient-specific latent anatomy model, Proc MICCAI-PMMIA, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00616174

T. Riklin-raviv, K. Van-leemput, B. H. Menze, W. M. Wells, G. et al., Segmentation of image ensembles via latent atlases. Medical Image Analysis, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00616108

R. Rockne, A. Jr, E. Rockhill, J. Swanson, and K. , A mathematical model for brain tumor response to radiation therapy, Journal of Mathematical Biology, vol.246, issue.(Suppl 13), pp.4-5561, 2009.
DOI : 10.1007/s00285-008-0219-6

T. Rohlfing, N. M. Zahr, E. V. Sullivan, and A. Pfefferbaum, The SRI24 multichannel atlas of normal adult human brain structure, Human Brain Mapping, vol.20, issue.Suppl S, pp.31798-819, 2010.
DOI : 10.1002/hbm.20906

T. Roose, S. J. Chapman, and P. K. Maini, Mathematical Models of Avascular Tumor Growth, SIAM Review, vol.49, issue.2, pp.179-208, 2007.
DOI : 10.1137/S0036144504446291

A. Rosset, L. Spadola, R. , and O. , OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images, Journal of Digital Imaging, vol.216, issue.Suppl 1, pp.205-216, 2004.
DOI : 10.1007/s10278-004-1014-6

N. Sanai and M. Berger, Glioma Extent of Resection and Its Impact on Patient Outcome, Neurosurgery, vol.62, issue.4, p.753, 2008.
DOI : 10.1227/01.neu.0000318159.21731.cf

S. Sanga, H. Frieboes, X. Zheng, R. Gatenby, E. Bearer et al., Predictive oncology: A review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth, NeuroImage, vol.37, pp.120-134, 2007.
DOI : 10.1016/j.neuroimage.2007.05.043

P. Schmitt, E. Mandonnet, A. Perdreau, and E. D. Angelini, Effects of slice thickness and head rotation when measuring glioma sizes on MRI: in support of volume segmentation versus two largest diameters methods, Journal of Neuro-Oncology, vol.193, issue.6, pp.1-8, 2013.
DOI : 10.1007/s11060-013-1051-4

J. A. Sethian, Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, 1999.

G. D. Shah, S. Kesari, R. Xu, T. T. Batchelor, A. M. Neill et al., Comparison of linear and volumetric criteria in assessing tumor response in adult high-grade gliomas, Neuro-Oncology, vol.8, issue.1, pp.38-46, 2006.
DOI : 10.1215/S1522851705000529

D. L. Silbergeld and M. R. Chicoine, Isolation and characterization of human malignant glioma cells from histologically normal brain, Journal of Neurosurgery, vol.86, issue.3, pp.525-531, 1997.
DOI : 10.3171/jns.1997.86.3.0525

R. Smith and S. M. , Fast robust automated brain extraction, Human Brain Mapping, vol.20, issue.3, pp.143-155, 2002.
DOI : 10.1002/hbm.10062

R. Soffietti, B. Baumert, L. Bello, V. Deimling, A. Duffau et al., Guidelines on management of low-grade gliomas: report of an EFNS-EANO* Task Force, European Journal of Neurology, vol.27, issue.9, pp.1124-1133, 2010.
DOI : 10.1111/j.1468-1331.2010.03151.x

A. Sottoriva, J. Verhoeff, T. Borovski, S. Mcweeney, L. Naumov et al., Cancer Stem Cell Tumor Model Reveals Invasive Morphology and Increased Phenotypical Heterogeneity, Cancer Research, vol.70, issue.1, p.46, 2010.
DOI : 10.1158/0008-5472.CAN-09-3663

E. Stretton, E. Geremia, B. Menze, H. Delingette, and N. Ayache, Importance of patient DTI's to accurately model glioma growth using the reaction diffusion equation, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.1142-1145, 2013.
DOI : 10.1109/ISBI.2013.6556681

E. Stretton, E. Mandonnet, E. Geremia, B. Menze, H. Delingette et al., Predicting the location of glioma recurrence after a resection surgery. Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data (MICCAI), pp.113-123, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00813870

K. Swanson, Mathematical Modeling of the Growth and Control of Tumors, 1999.

K. Swanson, E. Alvord, M. , and J. , A quantitative model for differential motility of gliomas in grey and white matter, Cell Proliferation, vol.29, issue.5, pp.317-330, 2000.
DOI : 10.1046/j.1365-2184.2000.00177.x

K. Swanson, C. Bridge, J. Murray, A. , and E. , Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion, Journal of the Neurological Sciences, vol.216, issue.1, pp.1-10, 2003.
DOI : 10.1016/j.jns.2003.06.001

K. Swanson, R. Rostomily, A. , and E. , A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle, British Journal of Cancer, vol.170, issue.1, pp.113-119, 2007.
DOI : 10.1002/(SICI)1096-9098(199912)72:4<199::AID-JSO4>3.0.CO;2-O

K. R. Swanson, E. C. Alvord, M. , and J. D. , Quantifying efficacy of chemotherapy of brain tumors with homogeneous and heterogeneous drug delivery, Acta Biotheoretica, vol.50, issue.4, pp.223-237, 2002.
DOI : 10.1023/A:1022644031905

J. Talairach and P. Tournoux, Co-planar stereotaxic atlas of the human brain. 3-Dimensional proportional system: an approach to cerebral imaging, 1988.

P. Therasse, E. Eisenhauer, and J. Verweij, RECIST revisited: A review of validation studies on tumour assessment, European Journal of Cancer, vol.42, issue.8, pp.1031-1039, 2006.
DOI : 10.1016/j.ejca.2006.01.026

M. Tovi, MR imaging in cerebral gliomas analysis of tumour tissue components, Acta radiologica. Supplementum, vol.3841, 1993.

P. Tracqui, Biophysical models of tumour growth, Reports on Progress in Physics, vol.72, issue.5, p.56701, 2009.
DOI : 10.1088/0034-4885/72/5/056701

P. Tracqui, G. Cruywagen, D. Woodward, G. Bartoo, and J. M. Jr, A mathematical model of glioma growth: the effect of chemotherapy on spatio-temporal growth, Cell Proliferation, vol.32, issue.1, pp.17-31, 1995.
DOI : 10.1016/S0022-5193(87)80171-6

P. Tracqui, G. C. Cruywagen, D. E. Woodward, G. T. Bartoo, J. D. Murray et al., A mathematical model of glioma growth: the effect of chemotherapy on spatio-temporal growth, Cell Proliferation, vol.32, issue.1, pp.17-31, 1995.
DOI : 10.1016/S0022-5193(87)80171-6

J. Unkelbach, B. Menze, A. Motamedi, F. Dittmann, E. Konukoglu et al., Glioblastoma growth modeling for radiotherapy target delineation, IGRT MICCAI Workshop, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00813872

J. Unkelbach, B. H. Menze, E. Konukoglu, F. Dittmann, M. Le et al., Radiotherapy planning for glioblastoma based on a tumor growth model: improving target volume delineation, Physics in Medicine and Biology, vol.59, issue.3, p.747, 2014.
DOI : 10.1088/0031-9155/59/3/747
URL : https://hal.archives-ouvertes.fr/hal-00917869

M. Van-den-bent, J. Wefel, D. Schiff, M. Taphoorn, K. Jaeckle et al., Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas, The Lancet Oncology, vol.12, issue.6, pp.583-593, 2011.
DOI : 10.1016/S1470-2045(11)70057-2

T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, Diffeomorphic demons: Efficient non-parametric image registration, NeuroImage, vol.45, issue.1, pp.61-72, 2009.
DOI : 10.1016/j.neuroimage.2008.10.040
URL : https://hal.archives-ouvertes.fr/inserm-00349600

C. H. Wang, J. K. Rockhill, M. Mrugala, D. L. Peacock, A. Lai et al., Prognostic Significance of Growth Kinetics in Newly Diagnosed Glioblastomas Revealed by Combining Serial Imaging with a Novel Biomathematical Model, Cancer Research, vol.69, issue.23, pp.699133-9140, 2009.
DOI : 10.1158/0008-5472.CAN-08-3863

S. K. Warfield, K. H. Zou, W. , and W. M. , Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation, IEEE Transactions on Medical Imaging, vol.23, issue.7, pp.903-921, 2004.
DOI : 10.1109/TMI.2004.828354

K. E. Warren, N. Patronas, A. A. Aikin, P. S. Albert, and F. M. Balis, Comparison of One-, Two-, and Three-Dimensional Measurements of Childhood Brain Tumors, JNCI Journal of the National Cancer Institute, vol.93, issue.18, pp.931401-1405, 2001.
DOI : 10.1093/jnci/93.18.1401

R. Wasserman and R. Acharya, A patient-specific in vivo tumor model, Mathematical Biosciences, vol.136, issue.2, pp.111-140, 1996.
DOI : 10.1016/0025-5564(96)00045-4

P. Y. Wen, D. R. Macdonald, D. A. Reardon, T. F. Cloughesy, A. G. Sorensen et al., Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group, Journal of Clinical Oncology, vol.28, issue.11, pp.281963-1972, 2010.
DOI : 10.1200/JCO.2009.26.3541

I. Whittle, The dilemma of low grade glioma, Journal of Neurology, Neurosurgery & Psychiatry, vol.75, issue.suppl_2, pp.31-36, 2004.
DOI : 10.1136/jnnp.2004.040501

C. Wilson and M. Berger, The gliomas, 1999.

D. Woodward, J. Cook, P. Tracqui, G. Cruywagen, J. Murray et al., A mathematical model of glioma growth: the effect of extent of surgical resection, Cell Proliferation, vol.28, issue.6, pp.29269-288, 1996.
DOI : 10.1002/1097-0142(197208)30:2<594::AID-CNCR2820300241>3.0.CO;2-2

J. Wu, L. Zhou, W. Tang, Y. Mao, J. Hu et al., CLINICAL EVALUATION AND FOLLOW-UP OUTCOME OF DIFFUSION TENSOR IMAGING-BASED FUNCTIONAL NEURONAVIGATION, Neurosurgery, vol.61, issue.5, pp.61935-949, 2007.
DOI : 10.1227/01.neu.0000303189.80049.ab

G. Young, Advanced MRI of Adult Brain Tumors, Neurologic Clinics, vol.25, issue.4, pp.947-973, 2007.
DOI : 10.1016/j.ncl.2007.07.010

P. A. Yushkevich, J. Piven, C. Hazlett, H. Smith, R. Ho et al., User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, NeuroImage, vol.31, issue.3, pp.311116-1128, 2006.
DOI : 10.1016/j.neuroimage.2006.01.015

E. I. Zacharaki, D. Shen, D. , and C. , ORBIT: A Multiresolution Framework for Deformable Registration of Brain Tumor Images, IEEE Transactions on Medical Imaging, vol.27, issue.8, pp.1003-1020, 2008.
DOI : 10.1109/TMI.2008.916954

Y. Zhang, M. Brady, and S. Smith, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm, IEEE Transactions on Medical Imaging, vol.20, issue.1, pp.45-57, 2001.
DOI : 10.1109/42.906424