Computational Trials: Unraveling Motility Phenotypes, Progression Patterns, and Treatment Options for Glioblastoma Multiforme, PLOS ONE, vol.59, issue.15, 2016. ,
DOI : 10.1371/journal.pone.0146617.t007
URL : https://hal.archives-ouvertes.fr/hal-01396271
Response Classification Based on a Minimal Model of Glioblastoma Growth Is Prognostic for Clinical Outcomes and Distinguishes Progression from Pseudoprogression, Cancer Research, vol.73, issue.10, pp.2976-2986, 2013. ,
DOI : 10.1158/0008-5472.CAN-12-3588
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.9133-9140, 2009. ,
DOI : 10.1158/0008-5472.CAN-08-3863
Predicting the location of glioma recurrence after a resection surgery, " in Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, pp.113-123, 2012. ,
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
A mathematical model for brain tumor response to radiation therapy, Journal of Mathematical Biology, vol.246, issue.(Suppl 13), pp.4-5, 2009. ,
DOI : 10.1007/s00285-008-0219-6
CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005-2009, Neuro-Oncology, vol.14, issue.suppl 5, pp.1-49, 2005. ,
DOI : 10.1093/neuonc/nos218
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
Mathematical biology, 2002. ,
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
Virtual brain tumours (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy, British Journal of Cancer, vol.29, issue.1, pp.14-18, 2002. ,
DOI : 10.1046/j.1365-2184.2000.00177.x
Avascular growth, angiogenesis and vascular growth in solid tumours: The mathematical modelling of the stages of tumour development, Mathematical and Computer Modelling, vol.23, issue.6, pp.47-87, 1996. ,
DOI : 10.1016/0895-7177(96)00019-2
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
Tumor invasion margin on the Riemannian space of brain fibers, Medical Image Analysis, vol.16, issue.2, pp.361-373, 2012. ,
DOI : 10.1016/j.media.2011.10.001
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
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
Quantifying the Role of Angiogenesis in Malignant Progression of Gliomas: In Silico Modeling Integrates Imaging and Histology, Cancer Research, vol.71, issue.24, pp.7366-7375, 2011. ,
DOI : 10.1158/0008-5472.CAN-11-1399
A Multilayer Grow-or-Go Model for GBM: Effects of Invasive Cells and Anti-Angiogenesis on Growth, Bulletin of Mathematical Biology, vol.91, issue.Suppl 1, pp.2306-2333, 2014. ,
DOI : 10.1007/s11538-014-0007-y
URL : https://hal.archives-ouvertes.fr/hal-01038063
Modeling Tumor-Associated Edema in Gliomas during Anti-Angiogenic Therapy and Its Impact on Imageable Tumor, Frontiers in Oncology, vol.3, 2013. ,
DOI : 10.3389/fonc.2013.00066
Oedema-based model for diffuse low-grade gliomas: application to clinical cases under radiotherapy, Cell Proliferation, vol.625, issue.4, pp.369-380, 2014. ,
DOI : 10.1111/cpr.12114
The Evolution of Mathematical Modeling of Glioma Proliferation and Invasion, Journal of Neuropathology and Experimental Neurology, vol.66, issue.1, 2007. ,
DOI : 10.1097/nen.0b013e31802d9000
The BOBYQA algorithm for bound constrained optimization without derivatives, 2009. ,
GLISTR: Glioma Image Segmentation and Registration, IEEE Transactions on Medical Imaging, vol.31, issue.10, pp.1941-1954, 2012. ,
DOI : 10.1109/TMI.2012.2210558
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
Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to Eikonal-Diffusion models in cardiac electrophysiology, Progress in biophysics and molecular biology, pp.134-146, 2011. ,
DOI : 10.1016/j.pbiomolbio.2011.07.002
URL : https://hal.archives-ouvertes.fr/inria-00616198
Robust Image-Based Estimation of Cardiac Tissue Parameters and Their Uncertainty from Noisy Data, MICCAI, pp.9-16, 2014. ,
DOI : 10.1007/978-3-319-10470-6_2
Bayesian personalization of brain tumor growth model, MICCAI, 2015. ,
Importance of patient DTI's to accurately model glioma growth using the reaction diffusion equation, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.1130-1162, 2013. ,
DOI : 10.1109/ISBI.2013.6556681
Use of Diffusion Tensor Images in Glioma Growth Modeling for Radiotherapy Target Delineation, Multimodal Brain Image Analysis, pp.63-73, 2013. ,
DOI : 10.1007/978-3-319-02126-3_7
URL : https://hal.archives-ouvertes.fr/hal-00912667
Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images, NeuroImage, vol.17, issue.2, 2002. ,
DOI : 10.1006/nimg.2002.1132
Non-linear registration , aka spatial normalisation fmrib technical report tr07ja2, 2007. ,
FSL, NeuroImage, vol.62, issue.2, pp.782-790, 2012. ,
DOI : 10.1016/j.neuroimage.2011.09.015
URL : https://hal.archives-ouvertes.fr/inserm-01149484
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods, IEEE Transactions on Medical Imaging, vol.30, issue.9, pp.1617-1634, 2011. ,
DOI : 10.1109/TMI.2011.2138152
Segmentation of brain mr images through a hidden markov random field model and the expectationmaximization algorithm, IEEE TMI, vol.20, issue.1, pp.45-57, 2001. ,
Automatic cerebral and cerebellar hemisphere segmentation in 3D MRI: adaptive disconnection algorithm A note on the numerical approach for the reaction? diffusion problem to model the density of the tumor growth dynamics, Medical image analysis Computers & Mathematics with Applications, vol.14, issue.3, pp.360-372, 2010. ,
LBM-EP: Lattice-Boltzmann Method for Fast Cardiac Electrophysiology Simulation from 3D Images, MICCAI 2012, pp.33-40, 2012. ,
DOI : 10.1007/978-3-642-33418-4_5
Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors, IEEE Transactions on Medical Imaging, vol.34, issue.7, p.14, 2015. ,
DOI : 10.1109/TMI.2015.2406575
URL : https://hal.archives-ouvertes.fr/hal-01146319
Multiple-Relaxation-Time LBM for the convection and anisotropic diffusion equation, Journal of Computational Physics, vol.229, issue.20, 2010. ,
Viscous flow computations with the method of lattice Boltzmann equation, Progress in Aerospace Sciences, pp.329-367, 2003. ,
DOI : 10.1016/S0376-0421(03)00003-4
Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins, Medical Image Analysis, vol.14, issue.2, 2010. ,
DOI : 10.1016/j.media.2009.11.005
URL : https://hal.archives-ouvertes.fr/inria-00616107
Gaussian processes to speed up hybrid Monte Carlo for expensive Bayesian integrals, pp.651-659, 2003. ,
MCMC Using Hamiltonian Dynamics, Handbook of Markov Chain Monte Carlo, 2011. ,
DOI : 10.1201/b10905-6
Nested Sampling with Constrained Hamiltonian Monte Carlo, 2010. ,
DOI : 10.1063/1.3573613
Gaussian Processes in Machine Learning, 2006. ,
DOI : 10.1162/089976602317250933
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.1293-1322, 2001. ,
DOI : 10.1098/rstb.2001.0915
SELECTION AND ASSESSMENT OF PHENOMENOLOGICAL MODELS OF TUMOR GROWTH, Mathematical Models and Methods in Applied Sciences, vol.23, issue.07, 2013. ,
DOI : 10.1142/S0218202513500103
GPSSI: Gaussian process for sampling segmentations of images, MICCAI, 2015. ,
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
Radiotherapy planning for glioblastoma based on a tumor growth model: implications for spatial dose redistribution, Physics in Medicine and Biology, vol.59, issue.3, p.771, 2014. ,
DOI : 10.1088/0031-9155/59/3/771
URL : https://hal.archives-ouvertes.fr/hal-00917846