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Publications of year 2020

Books and proceedings

  1. Nicholas Ayache, Alain Damasio, Yuval Noah Harari, Cathy O'Neil, and Nicolas Revel. Nouvelle enquête sur l'intelligence artificielle, Champs Actuel. Flammarion, June 2020. 
    @book{ayache:hal-03111667,
TITLE = {{Nouvelle enqu{\^e}te sur l'intelligence artificielle}},
AUTHOR = {Ayache, Nicholas and Damasio, Alain and Harari, Yuval Noah and O'Neil, Cathy and Revel, Nicolas},
url-hal= {https://inria.hal.science/hal-03111667},
PUBLISHER = {{Flammarion}},
SERIES = {Champs Actuel},
YEAR = {2020},
MONTH = Jun,
HAL_ID = {hal-03111667},
HAL_VERSION = {v1},

}


  2. Xavier Pennec, Stephan Sommer, and Tom Fletcher. Riemannian Geometric Statistics in Medical Image Analysis. Academic Press, 2020. 
    @book{pennec:hal-02341896,
TITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
AUTHOR = {Pennec, Xavier and Sommer, Stephan and Fletcher, Tom},
url-hal= {https://inria.hal.science/hal-02341896},
EDITOR = {Pennec, Xavier and Sommer, Stefan and Fletcher, Tom},
PUBLISHER = {{Academic Press}},
YEAR = {2020},
DOI = {10.1016/C2017-0-01561-6},
HAL_ID = {hal-02341896},
HAL_VERSION = {v1},

}


Thesis

  1. Nicolas Cedilnik. Image-based Personalised Models of Cardiac Electrophysiology for Ventricular Tachycardia Therapy Planning. Theses, Université Côte d'Azur, December 2020. Keyword(s): Ventricular tachycardia, Model personalisation, Cardiac electrophysiology, Computed tomography, Tachycardie ventriculaire, Personnalisation de modèles, Électrophysiologie cardiaque, Imagerie tomodensitométrique. 
    @phdthesis{cedilnik:tel-03147908,
TITLE = {{Image-based Personalised Models of Cardiac Electrophysiology for Ventricular Tachycardia Therapy Planning}},
AUTHOR = {Cedilnik, Nicolas},
url-hal= {https://hal.science/tel-03147908},
NUMBER = {2020COAZ4097},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2020},
MONTH = Dec,
KEYWORDS = {Ventricular tachycardia ; Model personalisation ; Cardiac electrophysiology ; Computed tomography ; Tachycardie ventriculaire ; Personnalisation de mod{\`e}les ; {\'E}lectrophysiologie cardiaque ; Imagerie tomodensitom{\'e}trique},
TYPE = {Theses},
PDF = {https://hal.science/tel-03147908v2/file/2020COAZ4097.pdf},
HAL_ID = {tel-03147908},
HAL_VERSION = {v2},

}


  2. Julian Krebs. Robust medical image registration and motion modeling based on machine learning. Theses, Université Côte d'Azur, June 2020. Keyword(s): Medical Image Registration, Motion Modeling, Machine Learning, Varitiaonal Autoencoder, Sudden Cardiac Death Risk, Recalage d'images médicales, Modélisation du mouvement, Apprentissage profond. 
    @phdthesis{krebs:tel-02954033,
TITLE = {{Robust medical image registration and motion modeling based on machine learning}},
AUTHOR = {Krebs, Julian},
url-hal= {https://hal.science/tel-02954033},
NUMBER = {2020COAZ4032},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2020},
MONTH = Jun,
KEYWORDS = {Medical Image Registration ; Motion Modeling ; Machine Learning ; Varitiaonal Autoencoder ; Sudden Cardiac Death Risk ; Recalage d'images m{\'e}dicales ; Mod{\'e}lisation du mouvement ; Apprentissage profond},
TYPE = {Theses},
PDF = {https://hal.science/tel-02954033v2/file/2020COAZ4032.pdf},
HAL_ID = {tel-02954033},
HAL_VERSION = {v2},

}


  3. Marco Lorenzi. Modelling pathological processes from heterogeneous and high-dimensional biomedical data. Habilitation à diriger des recherches, UCA, January 2020. Keyword(s): statistical learning, medical imaging, brain, machine learning, Alzheimer's disease, federated learning, genetics, apprentissage statistique, imageie medicale, cerveau, apprentissage par ordinateurs, maladie d'Alzheimer, apprentissge féderé genetics. 
    @phdthesis{lorenzi:tel-03150585,
TITLE = {{Modelling pathological processes from heterogeneous and high-dimensional biomedical data}},
AUTHOR = {Lorenzi, Marco},
url-hal= {https://inria.hal.science/tel-03150585},
SCHOOL = {{UCA}},
YEAR = {2020},
MONTH = Jan,
KEYWORDS = {statistical learning ; medical imaging ; brain ; machine learning ; Alzheimer's disease ; federated learning ; genetics ; apprentissage statistique ; imageie medicale ; cerveau ; apprentissage par ordinateurs ; maladie d'Alzheimer ; apprentissge f{\'e}der{\'e} genetics},
TYPE = {Habilitation {\`a} diriger des recherches},
PDF = {https://inria.hal.science/tel-03150585v1/file/hdr_2.pdf},
HAL_ID = {tel-03150585},
HAL_VERSION = {v1},

}


  4. Wen Wei. Learning brain alterations in multiple sclerosis from multimodal neuroimaging data. Theses, Université Côte d'Azur, June 2020. Keyword(s): Multiple Sclerosis, PET Imaging, MR Imaging, Brain Alterations, Deep Learning, Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Image Synthesis, Missing MRI Sequences, Missing Modalities, Sclérose en Plaques, TEP, IRM, Altérations cérébrales, Apprentissage en profondeur, Réseau de Neurones Convolutifs (CNN), Réseaux Antagonistes Génératifs (GAN), Synthèse d'images, Séquences IRM Manquantes, Modalités Manquantes. 
    @phdthesis{wei:tel-02862395,
TITLE = {{Learning brain alterations in multiple sclerosis from multimodal neuroimaging data}},
AUTHOR = {Wei, Wen},
url-hal= {https://theses.hal.science/tel-02862395},
NUMBER = {2020COAZ4021},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2020},
MONTH = Jun,
KEYWORDS = {Multiple Sclerosis ; PET Imaging ; MR Imaging ; Brain Alterations ; Deep Learning ; Convolutional Neural Networks (CNN) ; Generative Adversarial Network (GAN) ; Image Synthesis ; Missing MRI Sequences ; Missing Modalities ; Scl{\'e}rose en Plaques ; TEP ; IRM ; Alt{\'e}rations c{\'e}r{\'e}brales ; Apprentissage en profondeur ; R{\'e}seau de Neurones Convolutifs (CNN) ; R{\'e}seaux Antagonistes G{\'e}n{\'e}ratifs (GAN) ; Synth{\`e}se d'images ; S{\'e}quences IRM Manquantes ; Modalit{\'e}s Manquantes},
TYPE = {Theses},
PDF = {https://theses.hal.science/tel-02862395v2/file/2020COAZ4021.pdf},
HAL_ID = {tel-02862395},
HAL_VERSION = {v2},

}


Articles in journal, book chapters

  1. Benoît Audelan and Hervé Delingette. Unsupervised quality control of segmentations based on a smoothness and intensity probabilistic model. Medical Image Analysis, 68:101895, November 2020. Keyword(s): Unsupervised quality control, Image segmentation, Bayesian learning, Unsupervised quality control. 
    @article{audelan:hal-03044140,
TITLE = {{Unsupervised quality control of segmentations based on a smoothness and intensity probabilistic model}},
AUTHOR = {Audelan, Beno{\^i}t and Delingette, Herv{\'e}},
url-hal= {https://inria.hal.science/hal-03044140},
JOURNAL = {{Medical Image Analysis}},
PUBLISHER = {{Elsevier}},
VOLUME = {68},
PAGES = {101895},
YEAR = {2020},
MONTH = Nov,
DOI = {10.1016/j.media.2020.101895},
KEYWORDS = {Unsupervised quality control ; Image segmentation ; Bayesian learning ; Unsupervised quality control},
PDF = {https://inria.hal.science/hal-03044140v1/file/revised_manuscript.pdf},
HAL_ID = {hal-03044140},
HAL_VERSION = {v1},

}


  2. Paul Blanc-Durand, Jean-Baptiste. Schiratti, Kathryn Schutte, Paul Jehanno, Paul Herent, Frédéric Pigneur, Olivier Lucidarme, Y. Benaceur, Alexandre Sadate, Alain Luciani, Olivier Ernst, A. Rouchaud, Maud Creze, Alex Dallongeville, Nathan Banaste, Mehdi Cadi, Imad Bousaid, Nathalie Lassau, and Simon Jégou. Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment. Diagnostic and Interventional Imaging, 101(12):789-794, December 2020. Keyword(s): Convolutional neural networks (CNN), Deep learning, Muscular body bass, Sarcopenia, Tomography, X-ray computed. 
    @article{blancdurand:hal-03138538,
TITLE = {{Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment}},
AUTHOR = {Blanc-Durand, Paul and Schiratti, Jean-Baptiste. and Schutte, Kathryn and Jehanno, Paul and Herent, Paul and Pigneur, Fr{\'e}d{\'e}ric and Lucidarme, Olivier and Benaceur, Y. and Sadate, Alexandre and Luciani, Alain and Ernst, Olivier and Rouchaud, A. and Creze, Maud and Dallongeville, Alex and Banaste, Nathan and Cadi, Mehdi and Bousaid, Imad and Lassau, Nathalie and J{\'e}gou, Simon},
url-hal= {https://hal.science/hal-03138538},
JOURNAL = {{Diagnostic and Interventional Imaging}},
PUBLISHER = {{Elsevier}},
VOLUME = {101},
NUMBER = {12},
PAGES = {789-794},
YEAR = {2020},
MONTH = Dec,
DOI = {10.1016/j.diii.2020.04.011},
KEYWORDS = {Convolutional neural networks (CNN) ; Deep learning ; Muscular body bass ; Sarcopenia ; Tomography ; X-ray computed},
PDF = {https://hal.science/hal-03138538v1/file/S2211568420301224.pdf},
HAL_ID = {hal-03138538},
HAL_VERSION = {v1},

}


  3. Emmanuel Chevallier and Nicolas Guigui. A Bi-Invariant Statistical Model Parametrized by Mean and Covariance on Rigid Motions. Entropy, 22(4):432, 2020. Keyword(s): moment-matching estimator, Euclidean groups, density estimation, wrapped distributions, rigid motions, sampling, differential of the exponential. 
    @article{chevallier:hal-02568245,
TITLE = {{A Bi-Invariant Statistical Model Parametrized by Mean and Covariance on Rigid Motions}},
AUTHOR = {Chevallier, Emmanuel and Guigui, Nicolas},
url-hal= {https://hal.science/hal-02568245},
JOURNAL = {{Entropy}},
PUBLISHER = {{MDPI}},
VOLUME = {22},
NUMBER = {4},
PAGES = {432},
YEAR = {2020},
DOI = {10.3390/e22040432},
KEYWORDS = {moment-matching estimator ; Euclidean groups ; density estimation ; wrapped distributions ; rigid motions ; sampling ; differential of the exponential},
PDF = {https://hal.science/hal-02568245v1/file/SEnHAL_Chevallier_Guigui.pdf},
HAL_ID = {hal-02568245},
HAL_VERSION = {v1},

}


  4. James S Duncan, Michel F Insana, and Nicholas Ayache. Biomedical Imaging and Analysis In the Age of Big Data and Deep Learning. Proceedings of the IEEE, 14:3-10, January 2020. 
    @article{duncan:hal-02395429,
TITLE = {{Biomedical Imaging and Analysis In the Age of Big Data and Deep Learning}},
AUTHOR = {Duncan, James S and Insana, Michel F and Ayache, Nicholas},
url-hal= {https://hal.science/hal-02395429},
JOURNAL = {{Proceedings of the IEEE}},
PUBLISHER = {{Institute of Electrical and Electronics Engineers}},
VOLUME = {14},
PAGES = {3-10},
YEAR = {2020},
MONTH = Jan,
PDF = {https://hal.science/hal-02395429v2/file/Proceedings_of_IEEE_Editors_Overview_Article-10.pdf},
HAL_ID = {hal-02395429},
HAL_VERSION = {v2},

}


  5. Simon Heeke, Jonathan Benzaquen, Véronique Hofman, Elodie Long-Mira, Virginie Lespinet, Olivier Bordone, Charles-Hugo Marquette, Hervé Delingette, Marius Ilié, and Paul Hofman. Comparison of Three Sequencing Panels Used for the Assessment of Tumor Mutational Burden in NSCLC Reveals Low Comparability. Journal of Thoracic Oncology, 15(9):1535-1540, September 2020. 
    @article{heeke:hal-02972033,
TITLE = {{Comparison of Three Sequencing Panels Used for the Assessment of Tumor Mutational Burden in NSCLC Reveals Low Comparability}},
AUTHOR = {Heeke, Simon and Benzaquen, Jonathan and Hofman, V{\'e}ronique and Long-Mira, Elodie and Lespinet, Virginie and Bordone, Olivier and Marquette, Charles-Hugo and Delingette, Herv{\'e} and Ili{\'e}, Marius and Hofman, Paul},
url-hal= {https://inria.hal.science/hal-02972033},
JOURNAL = {{Journal of Thoracic Oncology}},
PUBLISHER = {{Lippincott, Williams \& Wilkins}},
VOLUME = {15},
NUMBER = {9},
PAGES = {1535-1540},
YEAR = {2020},
MONTH = Sep,
DOI = {10.1016/j.jtho.2020.05.013},
HAL_ID = {hal-02972033},
HAL_VERSION = {v1},

}


  6. Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, and Xavier Pennec. Geomstats: A Python Package for Riemannian Geometry in Machine Learning. Journal of Machine Learning Research, 21(223):1-9, December 2020. Keyword(s): differential geometry, Riemannian geometry, statistics, machine learning, manifold. 
    @article{miolane:hal-02536154,
TITLE = {{Geomstats: A Python Package for Riemannian Geometry in Machine Learning}},
AUTHOR = {Miolane, Nina and Guigui, Nicolas and Le Brigant, Alice and Mathe, Johan and Hou, Benjamin and Thanwerdas, Yann and Heyder, Stefan and Peltre, Olivier and Koep, Niklas and Zaatiti, Hadi and Hajri, Hatem and Cabanes, Yann and Gerald, Thomas and Chauchat, Paul and Shewmake, Christian and Brooks, Daniel and Kainz, Bernhard and Donnat, Claire and Holmes, Susan and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-02536154},
JOURNAL = {{Journal of Machine Learning Research}},
HAL_LOCAL_REFERENCE = {EXPLO},
PUBLISHER = {{Microtome Publishing}},
VOLUME = {21},
NUMBER = {223},
PAGES = {1-9},
YEAR = {2020},
MONTH = Dec,
KEYWORDS = {differential geometry ; Riemannian geometry ; statistics ; machine learning ; manifold},
PDF = {https://inria.hal.science/hal-02536154v2/file/19-027.pdf},
HAL_ID = {hal-02536154},
HAL_VERSION = {v2},

}


  7. Pawel Mlynarski, Hervé Delingette, Hamza A Alghamdi, Pierre-Yves Bondiau, and Nicholas Ayache. Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy. Journal of Medical Imaging, 7(1):1-21, February 2020. Keyword(s): segmentation, organs at risk, Convolutional Neural Networks, radiotherapy, MRI. 
    @article{mlynarski:hal-02181181,
TITLE = {{Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy}},
AUTHOR = {Mlynarski, Pawel and Delingette, Herv{\'e} and Alghamdi, Hamza A and Bondiau, Pierre-Yves and Ayache, Nicholas},
url-hal= {https://inria.hal.science/hal-02181181},
JOURNAL = {{Journal of Medical Imaging}},
PUBLISHER = {{SPIE Digital Library}},
VOLUME = {7},
NUMBER = {1},
PAGES = {1--21},
YEAR = {2020},
MONTH = Feb,
DOI = {10.1117/1.JMI.7.1.014502},
KEYWORDS = {segmentation ; organs at risk ; Convolutional Neural Networks ; radiotherapy ; MRI},
PDF = {https://inria.hal.science/hal-02181181v2/file/article.pdf},
HAL_ID = {hal-02181181},
HAL_VERSION = {v2},

}


  8. Pamela Moceri, Nicolas Duchateau, Stéphane Gillon, Lolita Jaunay, Delphine Baudouy, Fabien Squara, Emile Ferrari, and Maxime Sermesant. 3D right ventricular shape and strain in congenital heart disease patients with right ventricular chronic volume loading. European Heart Journal - Cardiovascular Imaging, 22(10):1174-1181, 2020. 
    @article{moceri:hal-02913107,
TITLE = {{3D right ventricular shape and strain in congenital heart disease patients with right ventricular chronic volume loading}},
AUTHOR = {Moceri, Pamela and Duchateau, Nicolas and Gillon, St{\'e}phane and Jaunay, Lolita and Baudouy, Delphine and Squara, Fabien and Ferrari, Emile and Sermesant, Maxime},
url-hal= {https://hal.science/hal-02913107},
JOURNAL = {{European Heart Journal - Cardiovascular Imaging}},
PUBLISHER = {{Oxford UP}},
VOLUME = {22},
NUMBER = {10},
PAGES = {1174-1181},
YEAR = {2020},
DOI = {10.1093/ehjci/jeaa189},
PDF = {https://hal.science/hal-02913107v1/file/Moceri_EHJCI_2020.pdf},
HAL_ID = {hal-02913107},
HAL_VERSION = {v1},

}


  9. Fanny Orlhac, Augustin Lecler, Julien Savatovski, Jessica Goya-Outi, Christophe Nioche, Frédérique Charbonneau, Nicholas Ayache, Frédérique Frouin, Loïc Duron, and Irène Buvat. How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. European Radiology, 2020. 
    @article{orlhac:hal-02945627,
TITLE = {{How can we combat multicenter variability in MR radiomics? Validation of a correction procedure}},
AUTHOR = {Orlhac, Fanny and Lecler, Augustin and Savatovski, Julien and Goya-Outi, Jessica and Nioche, Christophe and Charbonneau, Fr{\'e}d{\'e}rique and Ayache, Nicholas and Frouin, Fr{\'e}d{\'e}rique and Duron, Lo{\"i}c and Buvat, Ir{\`e}ne},
url-hal= {https://hal.science/hal-02945627},
JOURNAL = {{European Radiology}},
PUBLISHER = {{Springer Verlag}},
YEAR = {2020},
PDF = {https://hal.science/hal-02945627v1/file/Manuscrit_HAL.pdf},
HAL_ID = {hal-02945627},
HAL_VERSION = {v1},

}


  10. Raphaël Sivera, Nicolas Capet, Valeria Manera, Roxane Fabre, Marco Lorenzi, Hervé Delingette, Xavier Pennec, Nicholas Ayache, and Philippe Robert. Voxel-based assessments of treatment effects on longitudinal brain changes in the Multidomain Alzheimer Preventive Trial cohort. Neurobiology of Aging, 94:50-59, October 2020. Keyword(s): Multidomain intervention, Clinical trial, Subjective memory complaint, Deformation-based morphometry. 
    @article{sivera:hal-02166357,
TITLE = {{Voxel-based assessments of treatment effects on longitudinal brain changes in the Multidomain Alzheimer Preventive Trial cohort}},
AUTHOR = {Sivera, Rapha{\"e}l and Capet, Nicolas and Manera, Valeria and Fabre, Roxane and Lorenzi, Marco and Delingette, Herv{\'e} and Pennec, Xavier and Ayache, Nicholas and Robert, Philippe},
url-hal= {https://inria.hal.science/hal-02166357},
JOURNAL = {{Neurobiology of Aging}},
PUBLISHER = {{Elsevier}},
VOLUME = {94},
PAGES = {50-59},
YEAR = {2020},
MONTH = Oct,
DOI = {10.1016/j.neurobiolaging.2019.11.020},
KEYWORDS = {Multidomain intervention ; Clinical trial ; Subjective memory complaint ; Deformation-based morphometry},
PDF = {https://inria.hal.science/hal-02166357v2/file/paper-mapt-final.pdf},
HAL_ID = {hal-02166357},
HAL_VERSION = {v2},

}


  11. Wen Wei, Emilie Poirion, Benedetta Bodini, Matteo Tonietto, Stanley Durrleman, Olivier Colliot, Bruno Stankoff, and Nicholas Ayache. Predicting PET-derived Myelin Content from Multisequence MRI for Individual Longitudinal Analysis in Multiple Sclerosis. NeuroImage, 223C(117308), December 2020. Keyword(s): Conditional GANs, Attention Mechanism, PET Imaging, Multisequence MRI, Demyelination and Remyelination, Deep Learning, Multiple Sclerosis. 
    @article{wei:hal-02922534,
TITLE = {{Predicting PET-derived Myelin Content from Multisequence MRI for Individual Longitudinal Analysis in Multiple Sclerosis}},
AUTHOR = {Wei, Wen and Poirion, Emilie and Bodini, Benedetta and Tonietto, Matteo and Durrleman, Stanley and Colliot, Olivier and Stankoff, Bruno and Ayache, Nicholas},
url-hal= {https://inria.hal.science/hal-02922534},
JOURNAL = {{NeuroImage}},
PUBLISHER = {{Elsevier}},
VOLUME = {223C},
NUMBER = {117308},
YEAR = {2020},
MONTH = Dec,
DOI = {10.1016/j.neuroimage.2020.117308},
KEYWORDS = {Conditional GANs ; Attention Mechanism ; PET Imaging ; Multisequence MRI ; Demyelination and Remyelination ; Deep Learning ; Multiple Sclerosis},
PDF = {https://inria.hal.science/hal-02922534v1/file/elsarticle-template.pdf},
HAL_ID = {hal-02922534},
HAL_VERSION = {v1},

}


  12. Qiao Zheng, Hervé Delingette, Kenneth Fung, Steffen E Petersen, and Nicholas Ayache. Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs. Frontiers in Cardiovascular Medicine, 7:164, November 2020. Keyword(s): Cluster analysis, Gaussian mixture model, Cine MRI, Cardiac pathology, Feature extraction, UK Biobank, Cardiac motion. 
    @article{zheng:hal-02043380,
TITLE = {{Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs}},
AUTHOR = {Zheng, Qiao and Delingette, Herv{\'e} and Fung, Kenneth and Petersen, Steffen E and Ayache, Nicholas},
url-hal= {https://inria.hal.science/hal-02043380},
JOURNAL = {{Frontiers in Cardiovascular Medicine}},
PUBLISHER = {{Frontiers Media}},
VOLUME = {7},
PAGES = {164},
YEAR = {2020},
MONTH = Nov,
DOI = {10.3389/fcvm.2020.539788},
KEYWORDS = {Cluster analysis ; Gaussian mixture model ; Cine MRI ; Cardiac pathology ; Feature extraction ; UK Biobank ; Cardiac motion},
PDF = {https://inria.hal.science/hal-02043380v1/file/1902.05811.pdf},
HAL_ID = {hal-02043380},
HAL_VERSION = {v1},

}


  13. Nicholas Ayache. Medical Imaging in the Age of Artificial Intelligence. In Nordlinger B., Villani C., and Rus D., editors, Healthcare and Artificial Intelligence, pages 89-91. Springer International Publishing, March 2020. 
    @incollection{ayache:hal-02522507,
TITLE = {{Medical Imaging in the Age of Artificial Intelligence}},
AUTHOR = {Ayache, Nicholas},
url-hal= {https://inria.hal.science/hal-02522507},
BOOKTITLE = {{Healthcare and Artificial Intelligence}},
EDITOR = {Nordlinger B. and Villani C. and Rus D.},
PUBLISHER = {{Springer International Publishing}},
PAGES = {89-91},
YEAR = {2020},
MONTH = Mar,
DOI = {10.1007/978-3-030-32161-1\_13},
PDF = {https://inria.hal.science/hal-02522507v1/file/Ayache-Nordlinger2020-AuthorVersion.pdf},
HAL_ID = {hal-02522507},
HAL_VERSION = {v1},

}


  14. Nina Miolane, Loïc Devilliers, and Xavier Pennec. Bias on estimation in quotient space and correction methods: Applications to statistics on organ shapes. In Riemannian Geometric Statistics in Medical Image Analysis, number Chap. 9, pages 343-376. Elsevier, 2020. 
    @incollection{miolane:hal-02342155,
TITLE = {{Bias on estimation in quotient space and correction methods: Applications to statistics on organ shapes}},
AUTHOR = {Miolane, Nina and Devilliers, Lo{\"i}c and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-02342155},
BOOKTITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
PUBLISHER = {{Elsevier}},
NUMBER = {Chap. 9},
PAGES = {343-376},
YEAR = {2020},
DOI = {10.1016/B978-0-12-814725-2.00017-0},
PDF = {https://inria.hal.science/hal-02342155v1/file/chapterQuotient.pdf},
HAL_ID = {hal-02342155},
HAL_VERSION = {v1},

}


  15. Xavier Pennec. Advances in Geometric Statistics for Manifold Dimension Reduction. In Handbook of Variational Methods for Nonlinear Geometric Data, pages 339-359. Springer International Publishing, April 2020. 
    @incollection{pennec:hal-02560938,
TITLE = {{Advances in Geometric Statistics for Manifold Dimension Reduction}},
AUTHOR = {Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-02560938},
BOOKTITLE = {{Handbook of Variational Methods for Nonlinear Geometric Data}},
PUBLISHER = {{Springer International Publishing}},
PAGES = {339-359},
YEAR = {2020},
MONTH = Apr,
DOI = {10.1007/978-3-030-31351-7\_11},
PDF = {https://inria.hal.science/hal-02560938v1/file/XP_GeomStats_ManifoldReductionPCA.pdf},
HAL_ID = {hal-02560938},
HAL_VERSION = {v1},

}


  16. Xavier Pennec. Manifold-valued image processing with SPD matrices. In Riemannian Geometric Statistics in Medical Image Analysis, number Chap. 3, pages 75-134. Elsevier, 2020. 
    @incollection{pennec:hal-02341958,
TITLE = {{Manifold-valued image processing with SPD matrices}},
AUTHOR = {Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-02341958},
BOOKTITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
PUBLISHER = {{Elsevier}},
NUMBER = {Chap. 3},
PAGES = {75-134},
YEAR = {2020},
DOI = {10.1016/B978-0-12-814725-2.00010-8},
PDF = {https://inria.hal.science/hal-02341958v1/file/chapter3.pdf},
HAL_ID = {hal-02341958},
HAL_VERSION = {v1},

}


  17. Xavier Pennec and Marco Lorenzi. Beyond Riemannian geometry: The affine connection setting for transformation groups. In Riemannian Geometric Statistics in Medical Image Analysis, number Chap. 5, pages 169-229. Elsevier, 2020. 
    @incollection{pennec:hal-02342137,
TITLE = {{Beyond Riemannian geometry: The affine connection setting for transformation groups}},
AUTHOR = {Pennec, Xavier and Lorenzi, Marco},
url-hal= {https://inria.hal.science/hal-02342137},
BOOKTITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
PUBLISHER = {{Elsevier}},
NUMBER = {Chap. 5},
PAGES = {169-229},
YEAR = {2020},
DOI = {10.1016/B978-0-12-814725-2.00012-1},
PDF = {https://inria.hal.science/hal-02342137v1/file/chapter5.pdf},
HAL_ID = {hal-02342137},
HAL_VERSION = {v1},

}


  18. Stefan Sommer, Tom Fletcher, and Xavier Pennec. Introduction to differential and Riemannian geometry. In Riemannian Geometric Statistics in Medical Image Analysis, number Chap. 1, pages 3-37. Elsevier, 2020. 
    @incollection{sommer:hal-02341901,
TITLE = {{Introduction to differential and Riemannian geometry}},
AUTHOR = {Sommer, Stefan and Fletcher, Tom and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-02341901},
BOOKTITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
PUBLISHER = {{Elsevier}},
NUMBER = {Chap. 1},
PAGES = {3-37},
YEAR = {2020},
DOI = {10.1016/b978-0-12-814725-2.00008-x},
PDF = {https://inria.hal.science/hal-02341901v1/file/chapter1.pdf},
HAL_ID = {hal-02341901},
HAL_VERSION = {v1},

}


Conference articles

  1. Benoît Audelan, Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna, and Hervé Delingette. Robust Fusion of Probability Maps. In MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Lima/ Virtuel, Peru, October 2020. 
    @inproceedings{audelan:hal-02934590,
TITLE = {{Robust Fusion of Probability Maps}},
AUTHOR = {Audelan, Beno{\^i}t and Hamzaoui, Dimitri and Montagne, Sarah and Renard-Penna, Rapha{\"e}le and Delingette, Herv{\'e}},
url-hal= {https://inria.hal.science/hal-02934590},
BOOKTITLE = {{MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention}},
ADDRESS = {Lima/ Virtuel, Peru},
YEAR = {2020},
MONTH = Oct,
PDF = {https://inria.hal.science/hal-02934590v1/file/paper2268.pdf},
HAL_ID = {hal-02934590},
HAL_VERSION = {v1},

}


  2. Jaume Banus, Maxime Sermesant, Oscar Camara, and Marco Lorenzi. Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets. In MICCAI 2020 - 23th International Conference on Medical Image Computing and Computer Assisted Intervention, Lima / Virtual, Peru, pages 478-486, October 2020. Keyword(s): Gaussian Process, Variational Inference, Lumped model, Missing features, Biomechanical simulation. 
    @inproceedings{banus:hal-02952576,
TITLE = {{Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets}},
AUTHOR = {Banus, Jaume and Sermesant, Maxime and Camara, Oscar and Lorenzi, Marco},
url-hal= {https://inria.hal.science/hal-02952576},
BOOKTITLE = {{MICCAI 2020 - 23th International Conference on Medical Image Computing and Computer Assisted Intervention}},
ADDRESS = {Lima / Virtual, Peru},
PAGES = {478-486},
YEAR = {2020},
MONTH = Oct,
DOI = {10.1007/978-3-030-59725-2\_46},
KEYWORDS = {Gaussian Process ; Variational Inference ; Lumped model ; Missing features ; Biomechanical simulation},
PDF = {https://inria.hal.science/hal-02952576v2/file/MICCAI_2020_Jaume%2823%29.pdf},
HAL_ID = {hal-02952576},
HAL_VERSION = {v2},

}


  3. Emmanuel Chevallier and Nicolas Guigui. Wrapped statistical models on manifolds: motivations, the case SE(n), and generalization to symmetric spaces. In Joint Structures and Common Foundations of Statistical Physics, Information Geometry and Inference for Learning, Les Houches, France, July 2020. Keyword(s): Non-Euclidean statistics, wrapped distributions, exponential map, moment matching estimator. 
    @inproceedings{chevallier:hal-03154401,
TITLE = {{Wrapped statistical models on manifolds: motivations, the case SE(n), and generalization to symmetric spaces}},
AUTHOR = {Chevallier, Emmanuel and Guigui, Nicolas},
url-hal= {https://hal.science/hal-03154401},
BOOKTITLE = {{Joint Structures and Common Foundations of Statistical Physics, Information Geometry and Inference for Learning}},
ADDRESS = {Les Houches, France},
YEAR = {2020},
MONTH = Jul,
KEYWORDS = {Non-Euclidean statistics ; wrapped distributions ; exponential map ; moment matching estimator},
PDF = {https://hal.science/hal-03154401v1/file/WrappedModelsOnManifoldsChevallierGuigui.pdf},
HAL_ID = {hal-03154401},
HAL_VERSION = {v1},

}


  4. Dimitri Hamzaoui, Sarah Montagne, Pierre Mozer, Raphaële Renard-Penna, and Hervé Delingette. Segmentation automatique de la prostate à l'aide d'un réseau de neurones profond. In Congrès Francais d'Urologie, Paris, France, pages 696-697, November 2020. 
    @inproceedings{hamzaoui:hal-03126932,
TITLE = {{Segmentation automatique de la prostate {\`a} l'aide d'un r{\'e}seau de neurones profond}},
AUTHOR = {Hamzaoui, Dimitri and Montagne, Sarah and Mozer, Pierre and Renard-Penna, Rapha{\"e}le and Delingette, Herv{\'e}},
url-hal= {https://hal.science/hal-03126932},
BOOKTITLE = {{Congr{\`e}s Francais d'Urologie}},
ADDRESS = {Paris, France},
PAGES = {696-697},
YEAR = {2020},
MONTH = Nov,
DOI = {10.1016/j.purol.2020.07.010},
HAL_ID = {hal-03126932},
HAL_VERSION = {v1},

}


  5. Nina Miolane, Nicolas Guigui, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, Daniel Brooks, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Yann Cabanes, Thomas Gerald, Paul Chauchat, Bernhard Kainz, Claire Donnat, Susan Holmes, and Xavier Pennec. Introduction to Geometric Learning in Python with Geomstats. In Meghann Agarwal, Chris Calloway, Dillon Niederhut, and David Shupe, editors, SciPy 2020 - 19th Python in Science Conference, Austin, Texas, United States, pages 48-57, July 2020. Keyword(s): Index Terms-differential geometry, statistics, manifold, machine learning. 
    @inproceedings{miolane:hal-02908006,
TITLE = {{Introduction to Geometric Learning in Python with Geomstats}},
AUTHOR = {Miolane, Nina and Guigui, Nicolas and Zaatiti, Hadi and Shewmake, Christian and Hajri, Hatem and Brooks, Daniel and Le Brigant, Alice and Mathe, Johan and Hou, Benjamin and Thanwerdas, Yann and Heyder, Stefan and Peltre, Olivier and Koep, Niklas and Cabanes, Yann and Gerald, Thomas and Chauchat, Paul and Kainz, Bernhard and Donnat, Claire and Holmes, Susan and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-02908006},
BOOKTITLE = {{SciPy 2020 - 19th Python in Science Conference}},
ADDRESS = {Austin, Texas, United States},
HAL_LOCAL_REFERENCE = {EXPLO},
EDITOR = {Meghann Agarwal and Chris Calloway and Dillon Niederhut and David Shupe},
PAGES = {48-57},
YEAR = {2020},
MONTH = Jul,
DOI = {10.25080/Majora-342d178e-007},
KEYWORDS = {Index Terms-differential geometry ; statistics ; manifold ; machine learning},
PDF = {https://inria.hal.science/hal-02908006v1/file/geomstats.pdf},
HAL_ID = {hal-02908006},
HAL_VERSION = {v1},

}


  6. Pamela Moceri, Nicolas Duchateau, N Dursent, Xavier Iriart, Sébastien Hascoet, D Baudouy, Emile Ferrari, and Maxime Sermesant. Right ventricular remodelling in CHD-PAH patients using 3D speckle tracking. In JESFC 2020 - 30es Journées Européennes de la Société Française de Cardiologie, volume 12 of Archives of Cardiovascular Diseases Supplements, Paris, France, pages 163-4, January 2020. 
    @inproceedings{moceri:hal-02445303,
TITLE = {{Right ventricular remodelling in CHD-PAH patients using 3D speckle tracking}},
AUTHOR = {Moceri, Pamela and Duchateau, Nicolas and Dursent, N and Iriart, Xavier and Hascoet, S{\'e}bastien and Baudouy, D and Ferrari, Emile and Sermesant, Maxime},
url-hal= {https://hal.science/hal-02445303},
BOOKTITLE = {{JESFC 2020 - 30es Journ{\'e}es Europ{\'e}ennes de la Soci{\'e}t{\'e} Fran{\c c}aise de Cardiologie}},
ADDRESS = {Paris, France},
SERIES = {Archives of Cardiovascular Diseases Supplements},
VOLUME = {12},
PAGES = {163-4},
YEAR = {2020},
MONTH = Jan,
HAL_ID = {hal-02445303},
HAL_VERSION = {v1},

}


  7. Marta Nuñez-Garcia, Nicolas Cedilnik, Shuman Jia, Hubert Cochet, Marco Lorenzi, and Maxime Sermesant. Estimation of imaging biomarker's progression in post-infarct patients using cross-sectional data. In STACOM 2020 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart, Lima, Peru, pages p.108-116, October 2020. Keyword(s): Post-infarct cardiac remodeling, Ventricular arrhythmia, Cross-sectional data, Disease progression modeling. 
    @inproceedings{nunezgarcia:hal-02961506,
TITLE = {{Estimation of imaging biomarker's progression in post-infarct patients using cross-sectional data}},
AUTHOR = {Nu{\~n}ez-Garcia, Marta and Cedilnik, Nicolas and Jia, Shuman and Cochet, Hubert and Lorenzi, Marco and Sermesant, Maxime},
url-hal= {https://inria.hal.science/hal-02961506},
BOOKTITLE = {{STACOM 2020 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart}},
ADDRESS = {Lima, Peru},
PAGES = {p.108--116},
YEAR = {2020},
MONTH = Oct,
KEYWORDS = {Post-infarct cardiac remodeling ; Ventricular arrhythmia ; Cross-sectional data ; Disease progression modeling},
PDF = {https://inria.hal.science/hal-02961506v2/file/scar_maturation_stacom2020_cr.pdf},
HAL_ID = {hal-02961506},
HAL_VERSION = {v2},

}


  8. Marta Nuñez-Garcia, Nicolas Cedilnik, Shuman Jia, Maxime Sermesant, and Hubert Cochet. Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view. In STACOM 2020 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart, Lima, Peru, pages p.108-116, October 2020. Keyword(s): Automatic image reformatting, Short-axis view, Deep learning segmentation, Cardiac imaging. 
    @inproceedings{nunezgarcia:hal-02961500,
TITLE = {{Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view}},
AUTHOR = {Nu{\~n}ez-Garcia, Marta and Cedilnik, Nicolas and Jia, Shuman and Sermesant, Maxime and Cochet, Hubert},
url-hal= {https://inria.hal.science/hal-02961500},
BOOKTITLE = {{STACOM 2020 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart}},
ADDRESS = {Lima, Peru},
PAGES = {p.108--116},
YEAR = {2020},
MONTH = Oct,
KEYWORDS = {Automatic image reformatting ; Short-axis view ; Deep learning segmentation ; Cardiac imaging},
PDF = {https://inria.hal.science/hal-02961500v1/file/SAX_reformat_Stacom2020_cr.pdf},
HAL_ID = {hal-02961500},
HAL_VERSION = {v1},

}


  9. Fanny Orlhac, Thibaut Cassou-Mounat, Jean-Yves Pierga, Marie Luporsi, Christophe Nioche, Charles Bouveyron, Nicholas Ayache, Nina Jehanno, Alain Livartowski, and Irene Buvat. Can we identify ''twin patients'' to predict response to neoadjuvant chemotherapy in breast cancer?. In SNMMI Annual Meeting, Virtual Meeting, United States, July 2020. 
    @inproceedings{orlhac:inserm-02952453,
TITLE = {{Can we identify ''twin patients'' to predict response to neoadjuvant chemotherapy in breast cancer?}},
AUTHOR = {Orlhac, Fanny and Cassou-Mounat, Thibaut and Pierga, Jean-Yves and Luporsi, Marie and Nioche, Christophe and Bouveyron, Charles and Ayache, Nicholas and Jehanno, Nina and Livartowski, Alain and Buvat, Irene},
url-hal= {https://inserm.hal.science/inserm-02952453},
BOOKTITLE = {{SNMMI Annual Meeting}},
ADDRESS = {Virtual Meeting, United States},
YEAR = {2020},
MONTH = Jul,
PDF = {https://inserm.hal.science/inserm-02952453v1/file/Abstract_twin_HAL.pdf},
HAL_ID = {inserm-02952453},
HAL_VERSION = {v1},

}


  10. Fanny Orlhac, Anne-Capucine Rollet, Irène Buvat, Jacques Darcourt, Véronique Bourg, Christophe Nioche, Charles Bouveyron, Nicholas Ayache, and Olivier Humbert. Identifying a reliable radiomic signature from scarce data: illustration for 18F-FDOPA PET images in glioblastoma patients. In EANM Annual Meeting - Annual Meeting of the European Association of Nuclear Medicine, Virtual Meeting, Austria, October 2020. 
    @inproceedings{orlhac:inserm-02952445,
TITLE = {{Identifying a reliable radiomic signature from scarce data: illustration for 18F-FDOPA PET images in glioblastoma patients}},
AUTHOR = {Orlhac, Fanny and Rollet, Anne-Capucine and Buvat, Ir{\`e}ne and Darcourt, Jacques and Bourg, V{\'e}ronique and Nioche, Christophe and Bouveyron, Charles and Ayache, Nicholas and Humbert, Olivier},
url-hal= {https://inserm.hal.science/inserm-02952445},
BOOKTITLE = {{EANM Annual Meeting - Annual Meeting of the European Association of Nuclear Medicine}},
ADDRESS = {Virtual Meeting, Austria},
YEAR = {2020},
MONTH = Oct,
PDF = {https://inserm.hal.science/inserm-02952445v1/file/Absrract_Gliomes_HAL.pdf},
HAL_ID = {inserm-02952445},
HAL_VERSION = {v1},

}


  11. Santiago Silva, Andre Altmann, Boris Gutman, and Marco Lorenzi. Fed-BioMed: A general open-source frontendframework for federated learning in healthcare. In MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention - 1st Workshop on Distributed and Collaborative Learning, DCL: MICCAI Workshop on Distributed and Collaborative Learning, Lima/ Virtuel, Peru, pages 201-210, October 2020. Springer. Keyword(s): federated learning, healthcare, medical imaging. 
    @inproceedings{silva:hal-02966789,
TITLE = {{Fed-BioMed: A general open-source frontendframework for federated learning in healthcare}},
AUTHOR = {Silva, Santiago and Altmann, Andre and Gutman, Boris and Lorenzi, Marco},
url-hal= {https://hal.science/hal-02966789},
BOOKTITLE = {{MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention - 1st Workshop on Distributed and Collaborative Learning}},
ADDRESS = {Lima/ Virtuel, Peru},
PUBLISHER = {{Springer}},
SERIES = {DCL: MICCAI Workshop on Distributed and Collaborative Learning},
PAGES = {201-210},
YEAR = {2020},
MONTH = Oct,
KEYWORDS = {federated learning ; healthcare ; medical imaging},
PDF = {https://hal.science/hal-02966789v1/file/DCL_MICCAI_2020_Paper-1%281%29.pdf},
HAL_ID = {hal-02966789},
HAL_VERSION = {v1},

}


  12. Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, Nicolas Guevara, and Hervé Delingette. A Deep Learning based Fast Signed Distance Map Generation. In MIDL 2020 - Medical Imaging with Deep Learning, Montréal, Canada, July 2020. 
    @inproceedings{wang:hal-02570026,
TITLE = {{A Deep Learning based Fast Signed Distance Map Generation}},
AUTHOR = {Wang, Zihao and Vandersteen, Clair and Demarcy, Thomas and Gnansia, Dan and Raffaelli, Charles and Guevara, Nicolas and Delingette, Herv{\'e}},
url-hal= {https://inria.hal.science/hal-02570026},
BOOKTITLE = {{MIDL 2020 - Medical Imaging with Deep Learning}},
ADDRESS = {Montr{\'e}al, Canada},
YEAR = {2020},
MONTH = Jul,
PDF = {https://inria.hal.science/hal-02570026v1/file/MIDL2020__A_Deep_Learning_based_Fast_Signed_Distance_MapGeneration%20%281%29.pdf},
HAL_ID = {hal-02570026},
HAL_VERSION = {v1},

}


Patents, standards

  1. Julian Krebs and Tommaso Mansi. Method and System for Deep Motion Model Learning in Medical Images. US20200090345A1, United States, March 2020. 
    @patent{krebs:hal-02536459,
TITLE = {{Method and System for Deep Motion Model Learning in Medical Images}},
AUTHOR = {Krebs, Julian and Mansi, Tommaso},
url-hal= {https://hal.science/hal-02536459},
NUMBER = {US20200090345A1},
ADDRESS = {United States},
YEAR = {2020},
MONTH = Mar,
HAL_ID = {hal-02536459},
HAL_VERSION = {v1},

}


  2. Julian Krebs, Tommaso Mansi, Hervé Delingette, and Nicholas Ayache. Probabilist Motion Model for Generating Medical Images or Medical Image Sequences. US16834269, United States, October 2020. 
    @patent{krebs:hal-02977569,
TITLE = {{Probabilist Motion Model for Generating Medical Images or Medical Image Sequences}},
AUTHOR = {Krebs, Julian and Mansi, Tommaso and Delingette, Herv{\'e} and Ayache, Nicholas},
url-hal= {https://hal.science/hal-02977569},
NUMBER = {US16834269},
ADDRESS = {United States},
YEAR = {2020},
MONTH = Oct,
HAL_ID = {hal-02977569},
HAL_VERSION = {v1},

}


Miscellaneous

  1. Zihao Wang, Zhifei Xu, Jiayi He, Hervé Delingette, and Jun Fan. Long Short-Term Memory Neural Equalizer. Note: Working paper or preprint, November 2020. Keyword(s): decision feedback equalizer, neuromorphic computing, deep learning, LSTM. 
    @unpublished{wang:hal-03022865,
TITLE = {{Long Short-Term Memory Neural Equalizer}},
AUTHOR = {Wang, Zihao and Xu, Zhifei and He, Jiayi and Delingette, Herv{\'e} and Fan, Jun},
url-hal= {https://inria.hal.science/hal-03022865},
NOTE = {working paper or preprint},
YEAR = {2020},
MONTH = Nov,
KEYWORDS = {decision feedback equalizer ; neuromorphic computing ; deep learning ; LSTM},
PDF = {https://inria.hal.science/hal-03022865v1/file/Neuron_Equalizer__Copy_%20%282%29.pdf},
HAL_ID = {hal-03022865},
HAL_VERSION = {v1},

}



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