Introduction à l'imagerie biomédicale, Master 2 MMA, Université Paris Cité
Introduction à l'imagerie biomédicale, Master 2 MMA, Université Paris Cité
Le TP5 est à rendre d'ici vendredi 29 décembre 20h (aux formats ipynb et pdf). Cours1 TP1 TP2 Cours2 TP3 (Compléments Ex 2 : NB 1 , NB 2 ) ; TP4 imagesTP4 Cours3 TP5 imagesTP5 Cours4 Sources d'informations complémentaires : - COMPRENDRE L'IRM Manuel d'auto-apprentissage, 8ème édition, Bruno Kastler, Daniel Vetter - Page personnelle d'Isabelle Bloch (Telecom) - Page personnelle d'Irène Buvat (Inserm/CEA/UPSud) Imagerie microscopique : diaporama Le Journal du CNRS
Articles Imbio MMA
Articles Imbio MMA
Dates 2023 pour le projet :- Rapport : au plus tard le lundi 11 décembre- Transparents de la présentation au format pdf : samedi 16 décembre 20h (un envoi additionnel avec modifications mineures autorisé au plus tard lundi 18 décembre 11h)
Analyse d'articles :Pattern-Theoretic Characterization of Biological Growth,Ulf Grenander, Anuj Srivastava, and Sanjay Saini, IEEE TMI, 2007IMODAL: creating learnable user-defined deformation models, Leander Lacroix, Benjamin Charlier, Alain Trouvé, Barbara Gris 2021Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms, Michael I. Miller, Neuroimage 2004** Functional Currents : a new mathematical tool to model and analyse functional shapes, Nicolas Charon, and Alain Trouvé, Journal of mathematical imaging and vision - Springer, 2014Double diffeomorphism: combining morphometry and structural connectivity analysis, Pietro Gori et al., IEEE Transactions on Medical Imaging, 2018 Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles, Pietro Gori et al., IEEE Transactions on Medical Imaging, 2016Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing, Islem Rekik et al., Medical Image Analysis, 2016** A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations, Jean-Baptiste Schiratti et al., Journal of Machine Learning Research 18, 2017
Spatial Distribution of Deep Sulcal Landmarks and Hemispherical Asymmetry on the Cortical Surface, Kiho Im et al., Cerebral Cortex March 2010Model-Driven Harmonic Parameterization of the Cortical Surface: HIP-HOP, Guillaume Auzias et al., IEEE Transactions on Medical Imaging, 2013A Reaction-Diffusion Model of Human Brain Development, Julien Lefèvre, and Jean-François Mangin, PLoS computational biology, 2010SPANOL (SPectral ANalysis Of Lobes): A spectral clustering framework for individual and group parcellation of cortical surfaces in lobes, J Lefèvre et al., Frontiers in Brain Imaging Methods, 2018Fuzzy c-means clustering with spatial information for image segmentation, Keh-Shih Chuang et al, Computerized Medical Imaging and Graphics, 2016 (l'étude de cet article sera accompagnée d'un rappel sur les méthodes k-means et d'une implémentation et démonstration du modèle) Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration, B.T. Thomas Yeo et al., IEEE Trans Med Imaging. 2010U-Net: Convolutional Networks for Biomedical Image Segmentation, O. Ronneberger, P. Fischer, and T. Brox, MICCAI 2015CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction, Léo Lebrat et al., Neurips 2021
Analyse d'articles :Pattern-Theoretic Characterization of Biological Growth,Ulf Grenander, Anuj Srivastava, and Sanjay Saini, IEEE TMI, 2007IMODAL: creating learnable user-defined deformation models, Leander Lacroix, Benjamin Charlier, Alain Trouvé, Barbara Gris 2021Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms, Michael I. Miller, Neuroimage 2004** Functional Currents : a new mathematical tool to model and analyse functional shapes, Nicolas Charon, and Alain Trouvé, Journal of mathematical imaging and vision - Springer, 2014Double diffeomorphism: combining morphometry and structural connectivity analysis, Pietro Gori et al., IEEE Transactions on Medical Imaging, 2018 Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles, Pietro Gori et al., IEEE Transactions on Medical Imaging, 2016Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing, Islem Rekik et al., Medical Image Analysis, 2016** A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations, Jean-Baptiste Schiratti et al., Journal of Machine Learning Research 18, 2017
Spatial Distribution of Deep Sulcal Landmarks and Hemispherical Asymmetry on the Cortical Surface, Kiho Im et al., Cerebral Cortex March 2010Model-Driven Harmonic Parameterization of the Cortical Surface: HIP-HOP, Guillaume Auzias et al., IEEE Transactions on Medical Imaging, 2013A Reaction-Diffusion Model of Human Brain Development, Julien Lefèvre, and Jean-François Mangin, PLoS computational biology, 2010SPANOL (SPectral ANalysis Of Lobes): A spectral clustering framework for individual and group parcellation of cortical surfaces in lobes, J Lefèvre et al., Frontiers in Brain Imaging Methods, 2018Fuzzy c-means clustering with spatial information for image segmentation, Keh-Shih Chuang et al, Computerized Medical Imaging and Graphics, 2016 (l'étude de cet article sera accompagnée d'un rappel sur les méthodes k-means et d'une implémentation et démonstration du modèle) Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration, B.T. Thomas Yeo et al., IEEE Trans Med Imaging. 2010U-Net: Convolutional Networks for Biomedical Image Segmentation, O. Ronneberger, P. Fischer, and T. Brox, MICCAI 2015CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction, Léo Lebrat et al., Neurips 2021
Retiré pour 2023A general framework for curve and surface comparison and registration with oriented varifolds. Kaltenmark et al, CVPR 2017