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Olivier Debeir | Christine Decaestecker | Thierry Metens | Daniele Bonatto | Corentin Martens | Gaetan Van Simaeys | Antonin Rovai | Serge Goldman | C. Martens | O. Debeir | C. Decaestecker | T. Metens | A. Rovai | G. V. Simaeys | S. Goldman | Daniele Bonatto | Antonin Rovai
[1] Isabelle Salmon,et al. Initial Condition Assessment for Reaction-Diffusion Glioma Growth Models: A Translational MRI-Histology (In)Validation Study , 2021, Tomography.
[2] Christos Davatzikos,et al. Modeling Glioma Growth and Mass Effect in 3D MR Images of the Brain , 2007, MICCAI.
[3] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[4] R. Guillevin,et al. Simulation of anisotropic growth of low‐grade gliomas using diffusion tensor imaging , 2005, Magnetic resonance in medicine.
[5] Nicholas Ayache,et al. Radiotherapy planning for glioblastoma based on a tumor growth model: improving target volume delineation , 2013, Physics in medicine and biology.
[6] N. Otsu. A threshold selection method from gray level histograms , 1979 .
[7] J. Gore,et al. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. , 2014, Magnetic resonance imaging.
[8] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[9] J. Murray,et al. Quantifying Efficacy of Chemotherapy of Brain Tumors with Homogeneous and Heterogeneous Drug Delivery , 2002, Acta biotheoretica.
[10] Paul E Kinahan,et al. Applying a patient-specific bio-mathematical model of glioma growth to develop virtual [18F]-FMISO-PET images. , 2011, Mathematical medicine and biology : a journal of the IMA.
[11] Zhi-xiong Lin. Glioma-related edema: new insight into molecular mechanisms and their clinical implications , 2013, Chinese journal of cancer.
[12] Naeem Khalid Janjua,et al. Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions , 2019, IEEE Access.
[13] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[14] K. Swanson,et al. Modeling Tumor-Associated Edema in Gliomas during Anti-Angiogenic Therapy and Its Impact on Imageable Tumor , 2013, Front. Oncol..
[15] Vincent François-Lavet,et al. Reinforcement Learning for Radiotherapy Dose Fractioning Automation , 2021, Biomedicines.
[16] Christian Woiciechowsky,et al. Correlation of F-18-fluoro-ethyl-tyrosin uptake with vascular and cell density in non-contrast-enhancing gliomas , 2008, Journal of Neuro-Oncology.
[17] L. Lin,et al. A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.
[18] J. Murray,et al. A mathematical model of glioma growth: the effect of chemotherapy on spatio‐temporal growth , 1995, Cell proliferation.
[19] Rym Jaroudi,et al. Source Localization of Reaction-Diffusion Models for Brain Tumors , 2016, GCPR.
[20] J. Murray,et al. A quantitative model for differential motility of gliomas in grey and white matter , 2000, Cell proliferation.
[21] Michael J Ackerman,et al. Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.
[22] Glyn Johnson,et al. Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index. , 2004, Radiology.
[23] U. Naumann,et al. Molecular Mechanisms of Glioma Cell Motility , 2017 .
[24] Hervé Delingette,et al. A Recursive Anisotropic Fast Marching Approach to Reaction Diffusion Equation: Application to Tumor Growth Modeling , 2007, IPMI.
[25] Guy Marchal,et al. Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.
[26] J. C. Gore,et al. Incorporation of diffusion-weighted magnetic resonance imaging data into a simple mathematical model of tumor growth , 2012, Physics in medicine and biology.
[27] Hervé Delingette,et al. Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations , 2010, IEEE Transactions on Medical Imaging.
[28] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[29] Hervé Delingette,et al. Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation , 2005, IEEE Transactions on Medical Imaging.
[30] Hervé Delingette,et al. Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas , 2013, Comput. Vis. Image Underst..
[31] J. Murray,et al. A mathematical model of glioma growth: the effect of extent of surgical resection , 1996, Cell proliferation.
[32] Martin Jägersand,et al. Stability effects of finite difference methods on a mathematical tumor growth model , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.
[33] William J. Schroeder,et al. The Visualization Toolkit , 2005, The Visualization Handbook.
[34] John J Lannutti,et al. Quantitative analysis of complex glioma cell migration on electrospun polycaprolactone using time-lapse microscopy. , 2009, Tissue engineering. Part C, Methods.
[35] Cristóbal López,et al. Reaction-Diffusion Systems: Front Propagation and Spatial Structures , 2003 .
[36] Paheding Sidike,et al. U-Net and its variants for medical image segmentation: theory and applications , 2020, ArXiv.
[37] K Hendrickson,et al. Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach , 2010, Physics in medicine and biology.
[38] Hervé Delingette,et al. Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins , 2010, Medical Image Anal..
[39] K. Swanson,et al. A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle , 2007, British Journal of Cancer.
[40] Alexander R A Anderson,et al. Quantifying the Role of Angiogenesis in Malignant Progression of Gliomas: in Silico Modeling Integrates Imaging and Histology Nih Public Access Author Manuscript Introduction , 2011 .
[41] Hervé Delingette,et al. Bayesian Personalization of Brain Tumor Growth Model , 2015, MICCAI.
[42] Panagiotis Angelikopoulos,et al. Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference , 2018, IEEE Transactions on Medical Imaging.
[43] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[44] Mark W. Woolrich,et al. FSL , 2012, NeuroImage.
[45] R. Fisher. THE WAVE OF ADVANCE OF ADVANTAGEOUS GENES , 1937 .
[46] Hervé Delingette,et al. Importance of patient DTI's to accurately model glioma growth using the reaction diffusion equation , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.
[47] Christopher Rorden,et al. The first step for neuroimaging data analysis: DICOM to NIfTI conversion , 2016, Journal of Neuroscience Methods.
[48] T. Yankeelov,et al. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation , 2021, Scientific Reports.
[49] Christos Davatzikos,et al. An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects , 2008, Journal of mathematical biology.