Bibliography

Major publications by the team in recent years

1S. Giffard-Roisin, T. Jackson, L. Fovargue, J. Lee, H. Delingette, R. Razavi, N. Ayache, M. Sermesant.

Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping, in: IEEE Transactions on Biomedical Engineering, September 2017, vol. 64, n^o 9, pp. 2206 - 2218. [ DOI : 10.1109/TBME.2016.2629849 ]
2B. Khanal, N. Ayache, X. Pennec.

Simulating Longitudinal Brain MRIs with known Volume Changes and Realistic Variations in Image Intensity, in: Frontiers in Neuroscience, February 2017, vol. 11, n^o Article 132, 18 p. [ DOI : 10.3389/fnins.2017.00132 ]
3L. Le Folgoc, H. Delingette, A. Criminisi, N. Ayache.

Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements, in: Medical Image Analysis, February 2017, vol. 36, pp. 79 - 97. [ DOI : 10.1016/j.media.2016.09.008 ]
4M. Lorenzi, X. Pennec.

Geodesics, Parallel Transport & One-parameter Subgroups for Diffeomorphic Image Registration, in: International Journal of Computer Vision, November 2013, vol. 105, n^o 2, pp. 111-127. [ DOI : 10.1007/s11263-012-0598-4 ]
5M. Lê, H. Delingette, J. Kalpathy-Cramer, E. R. Gerstner, T. Batchelor, J. Unkelbach, N. Ayache.

MRI Based Bayesian Personalization of a Tumor Growth Model, in: IEEE Transactions on Medical Imaging, April 2016, vol. 35, n^o 10, pp. 2329-2339. [ DOI : 10.1109/TMI.2016.2561098 ]

Publications of the year

Doctoral Dissertations and Habilitation Theses

6T. Demarcy.

Segmentation and study of anatomical variability of the cochlea from medical images, Université Côte d'Azur, July 2017.

https://tel.archives-ouvertes.fr/tel-01609910
7L. Devilliers.

Consistency of statistics in infinite dimensional quotient spaces, Université Côte d’Azur, November 2017.

https://hal.inria.fr/tel-01683607
8S. Giffard-Roisin.

Non-invasive Personalisation of Cardiac Electrophysiological Models from Surface Electrograms, Université côte d'azur, December 2017.

https://hal.inria.fr/tel-01658489
9R. Molléro.

Robust Personalisation of 3D Electromechanical Cardiac Models. Application to Heterogeneous and Longitudinal Clinical Databases, Université Nice Côte d'Azur, December 2017.

https://hal.inria.fr/tel-01656290
10M.-M. Rohé.

Reduced representation of segmentation and tracking in cardiac images for group-wise longitudinal analysis, Université Côte d'Azur, July 2017.

https://hal.inria.fr/tel-01575292

Articles in International Peer-Reviewed Journals

11J. L. Bruse, A. Khushnood, K. Mcleod, G. Biglino, M. Sermesant, X. Pennec, A. M. Taylor, T.-Y. Hsia, S. Schievano.

How successful is successful? Aortic arch shape after successful aortic coarctation repair correlates with left ventricular function, in: Journal of Thoracic and Cardiovascular Surgery, February 2017, vol. 153, n^o 2, pp. 418 - 427. [ DOI : 10.1016/j.jtcvs.2016.09.018 ]

https://hal.inria.fr/hal-01387297
12J. L. Bruse, M. A. Zuluaga, A. Khushnood, K. Mcleod, H. N. Ntsinjana, T.-Y. Hsia, M. Sermesant, X. Pennec, A. M. Taylor, S. Schievano.

Detecting clinically meaningful shape clusters in medical image data: metrics analysis for hierarchical clustering applied to healthy and pathological aortic arches, in: IEEE Transactions on Biomedical Engineering, February 2017, pp. 1 - 13. [ DOI : 10.1109/TBME.2017.2655364 ]

https://hal.inria.fr/hal-01421202
13T. Demarcy, C. Vandersteen, N. Guevara, C. Raffaelli, D. Gnansia, N. Ayache, H. Delingette.

Automated analysis of human cochlea shape variability from segmented μCT images, in: Computerized Medical Imaging and Graphics, 2017, vol. 59, n^o July 2017, pp. 1 - 12. [ DOI : 10.1016/j.compmedimag.2017.04.002 ]

https://hal.inria.fr/hal-01528489
14L. Devilliers, S. Allassonnière, A. Trouvé, X. Pennec.

Inconsistency of template estimation by minimizing of the variance/pre-variance in the quotient space, in: Entropy, June 2017, vol. 19, n^o 6, 28 p, https://arxiv.org/abs/1706.10125. [ DOI : 10.3390/e19060288 ]

https://hal.inria.fr/hal-01543616
15L. Devilliers, S. Allassonnière, A. Trouvé, X. Pennec.

Template estimation in computational anatomy: Fréchet means in top and quotient spaces are not consistent, in: SIAM Journal on Imaging Sciences, August 2017, vol. 10, n^o 3, pp. 1139-1169, https://arxiv.org/abs/1608.03703. [ DOI : 10.1137/16M1083931 ]

https://hal.archives-ouvertes.fr/hal-01352707
16N. Duchateau, M. Sermesant, H. Delingette, N. Ayache.

Model-based generation of large databases of cardiac images: synthesis of pathological cine MR sequences from real healthy cases, in: IEEE Transactions on Medical Imaging, 2017, In press. [ DOI : 10.1109/TMI.2017.2714343 ]

https://hal.inria.fr/hal-01533788
17S. Giffard-Roisin, T. Jackson, L. Fovargue, J. Lee, H. Delingette, R. Razavi, N. Ayache, M. Sermesant.

Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping, in: IEEE Transactions on Biomedical Engineering, September 2017, vol. 64, n^o 9, pp. 2206 - 2218, Selected for the TBME highlights, sept. 2017https://tbme.embs.org/2017/08/24/non-invasive-personalisation-cardiac-electrophysiology-model-body-surface-potential-mapping/. [ DOI : 10.1109/TBME.2016.2629849 ]

https://hal.inria.fr/hal-01397393
18P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, C. Poupon, A. Hartmann, N. Ayache, S. Durrleman.

A Bayesian Framework for Joint Morphometry of Surface and Curve meshes in Multi-Object Complexes, in: Medical Image Analysis, January 2017, vol. 35, pp. 458-474. [ DOI : 10.1016/j.media.2016.08.011 ]

https://hal.inria.fr/hal-01359423
19B. Khanal, N. Ayache, X. Pennec.

Simulating Longitudinal Brain MRIs with known Volume Changes and Realistic Variations in Image Intensity, in: Frontiers in Neuroscience, February 2017, vol. 11, n^o Article 132, 18 p. [ DOI : 10.3389/fnins.2017.00132 ]

https://hal.inria.fr/hal-01348959
20L. Le Folgoc, H. Delingette, A. Criminisi, N. Ayache.

Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements, in: Medical Image Analysis, February 2017, vol. 36, pp. 79 - 97. [ DOI : 10.1016/j.media.2016.09.008 ]

https://hal.inria.fr/hal-01149544
21M. Lorenzi, M. Filippone, G. Frisoni, D. C. Alexander, S. Ourselin.

Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease, in: NeuroImage, October 2017.

https://hal.archives-ouvertes.fr/hal-01617750
22N. Miolane, S. Holmes, X. Pennec.

Template Shape Estimation: Correcting an Asymptotic Bias, in: SIAM Journal on Imaging Sciences, 2017, vol. 10, n^o 2, pp. 808 - 844, https://arxiv.org/abs/1610.01502. [ DOI : 10.1137/16M1084493 ]

https://hal.archives-ouvertes.fr/hal-01350508
23P. Moceri, N. Duchateau, D. Baudouy, E.-D. Schouver, S. Leroy, F. Squara, E. Ferrari, M. Sermesant.

Three-dimensional right-ventricular regional deformation and survival in pulmonary hypertension, in: European Heart Journal - Cardiovascular Imaging, 2017, In press. [ DOI : 10.1093/ehjci/jex163 ]

https://hal.inria.fr/hal-01533793
24R. Molléro, X. Pennec, H. Delingette, A. Garny, N. Ayache, M. Sermesant.

Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models, in: Biomechanics and Modeling in Mechanobiology, September 2017, pp. 1-16. [ DOI : 10.1007/s10237-017-0960-0 ]

https://hal.inria.fr/hal-01656008
25X. Pennec.

Barycentric Subspace Analysis on Manifolds, in: Annals of Statistics, 2017, https://arxiv.org/abs/1607.02833v2, forthcoming.

https://hal.archives-ouvertes.fr/hal-01343881
26N. Rodriguez-Florez, J. L. Bruse, A. Borghi, H. Vercruysse, J. Ong, G. James, X. Pennec, D. J. Dunaway, O. Jeelani, S. Schievano.

Statistical shape modelling to aid surgical planning: associations between surgical parameters and head shapes following spring-assisted cranioplasty, in: International Journal of Computer Assisted Radiology and Surgery, May 2017, pp. 1-11. [ DOI : 10.1007/s11548-017-1614-5 ]

https://hal.inria.fr/hal-01540631
27M.-M. Rohé, M. Sermesant, X. Pennec.

Low-Dimensional Representation of Cardiac Motion Using Barycentric Subspaces: a New Group-Wise Paradigm for Estimation, Analysis, and Reconstruction, in: Medical Image Analysis, April 2018, vol. 45, pp. 1-12. [ DOI : 10.1016/j.media.2017.12.008 ]

https://hal.inria.fr/hal-01677685
28S. Sanchez-Martinez, N. Duchateau, T. Erdei, A. Fraser, B. Bijnens, G. Piella.

Characterization of myocardial motion patterns by unsupervised multiple kernel learning, in: Medical Image Analysis, 2017, vol. 35, pp. 70–82. [ DOI : 10.1016/j.media.2016.06.007 ]

https://hal.inria.fr/hal-01331550
29W. Schulze, Z. Chen, J. Relan, D. Potyagaylo, M. W. Krueger, R. Karim, M. Sohal, A. Shetty, Y. Ma, N. Ayache, M. Sermesant, H. Delingette, J. Bostock, R. Razavi, K. S. Rhode, C. A. Rinaldi.

ECG imaging of ventricular tachycardia: evaluation against simultaneous non-contact mapping and CMR-derived grey zone, in: Medical and Biological Engineering and Computing, June 2017, vol. 55, n^o 6, pp. 979 - 990. [ DOI : 10.1007/s11517-016-1566-x ]

https://hal.inria.fr/hal-01598299
30D. Soto-Iglesias, N. Duchateau, C. Butakoff, D. Andreu, J. Fernández-Armenta, B. Bijnens, A. Berruezo, M. Sitges, O. Camara.

Quantitative analysis of electro-anatomical maps: application to an experimental model of LBBB/CRT, in: IEEE Journal of Translational Engineering in Health and Medicine, 2017, In press. [ DOI : 10.1109/JTEHM.2016.2634006 ]

https://hal.inria.fr/hal-01398828
31A. A. Suinesiaputra, P. A. Ablin, X. A. Albà, M. Alessandrini, J. A. Allen, W. Bai, S. Çimen, P. Claes, B. R. Cowan, J. D'Hooge, N. Duchateau, J. Ehrhardt, A. F. Frangi, A. A. Gooya, V. Grau, K. Lekadir, A. A. Lu, A. A. Mukhopadhyay, I. Oksuz, N. Parajuli, X. Pennec, M. Pereañez, C. Pinto, P. Piras, M.-M. Rohé, D. R. Rueckert, D. Säring, M. Sermesant, K. Siddiqi, M. Tabassian, L. Teresi, S. A. Tsaftaris, M. Wilms, A. A. Young, X. Zhang, P. Medrano-Gracia.

Statistical shape modeling of the left ventricle: myocardial infarct classification challenge, in: IEEE Journal of Biomedical and Health Informatics, 2017, 13 p, In press. [ DOI : 10.1109/JBHI.2017.2652449 ]

https://hal.inria.fr/hal-01533805
32Y. Zhou, S. Giffard-Roisin, M. De Craene, S. Camarasu-Pop, J. D'Hooge, M. Alessandrini, D. Friboulet, M. Sermesant, O. Bernard.

A Framework for the Generation of Realistic Synthetic Cardiac Ultrasound and Magnetic Resonance Imaging Sequences from the same Virtual Patients, in: IEEE Transactions on Medical Imaging, 2017. [ DOI : 10.1109/TMI.2017.2708159 ]

https://hal.inria.fr/hal-01533366

Invited Conferences

33R. Molléro, J. Hauser, X. Pennec, M. Datar, H. Delingette, A. Jones, N. Ayache, T. Heimann, M. Sermesant.

Longitudinal Parameter Estimation in 3D Electromechanical Models: Application to Cardiovascular Changes in Digestion, in: FIMH 2017 - 9th international conference on Functional Imaging and Modeling of the Heart, Toronto, Canada, Functional Imaging and Modelling of the Heart, Springer International Publishing, June 2017, pp. 432-440. [ DOI : 10.1007/978-3-319-59448-4_41 ]

https://hal.inria.fr/hal-01522598

International Conferences with Proceedings

34N. Cedilnik, J. Duchateau, R. Dubois, P. Jaïs, H. Cochet, M. Sermesant.

VT Scan: Towards an Efficient Pipeline from Computed Tomography Images to Ventricular Tachycardia Ablation, in: Functional Imaging and Modelling of the Heart, Toronto, Canada, Functional Imaging and Modelling of the Heart, Springer International Publishing, June 2017, pp. 271-279. [ DOI : 10.1007/978-3-319-59448-4_26 ]

https://hal.inria.fr/hal-01498672
35L. Devilliers, X. Pennec, S. Allassonnière.

Inconsistency of Template Estimation with the Fréchet mean in Quotient Space, in: Information Processing in Medical Imaging 2017, Boone, United States, Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings, Martin Styner and Marc Niethammer and Dinggang Shen and Stephen Aylward and Ipek Oguz and Hongtu Zhu, June 2017, 12 p, https://arxiv.org/abs/1703.01232. [ DOI : 10.1007/978-3-319-59050-9_2 ]

https://hal.inria.fr/hal-01481074
36R. Doste, D. Soto-Iglesias, G. Bernardino, R. Sebastian, S. Giffard-Roisin, R. Cabrera-Lozoya, M. Sermesant, A. Berruezo, D. Sánchez-Quintana, O. Camara.

A Rule-Based Method to Model Myocardial Fiber Orientation for Simulating Ventricular Outflow Tract Arrhythmias, in: Functional imaging and modelling of the heart 2017, Toronto, Canada, Lecture Notes in Computer Science, June 2017, vol. 10263. [ DOI : 10.1007/978-3-319-59448-4_33 ]

https://hal.inria.fr/hal-01533389
38S. Jia, C. Camaioni, M.-M. Rohé, P. Jaïs, X. Pennec, H. Cochet, M. Sermesant.

Prediction of Post-Ablation Outcome in Atrial Fibrillation Using Shape Parameterization and Partial Least Squares Regression, in: FIMH 2017 - International Conference on Functional Imaging and Modeling of the Heart, Toronto, Canada, Lecture Notes in Computer Science, June 2017, vol. 10263, pp. 314 - 321. [ DOI : 10.1007/978-3-319-59448-4_30 ]

https://hal.inria.fr/hal-01574831
39J. Krebs, T. Mansi, H. Delingette, L. Zhang, F. C. Ghesu, S. Miao, A. Maier, N. Ayache, R. Liao, A. Kamen.

Robust non-rigid registration through agent-based action learning, in: Medical Image Computing and Computer Assisted Interventions (MICCAI), Quebec, Canada, Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, Springer International Publishing, September 2017, pp. 344-352. [ DOI : 10.1007/978-3-319-66182-7_40 ]

https://hal.inria.fr/hal-01569447
40E. Lluch, R. Doste, S. Giffard-Roisin, A. This, M. Sermesant, O. Camara, M. De Craene, H. G. Morales.

Smoothed Particle Hydrodynamics for Electrophysiological Modeling: An Alternative to Finite Element Methods, in: Functional imaging and modelling of the heart 2017, Toronto, Canada, Functional imaging and modelling of the heart 2017 Proceedings, Springer International Publishing, June 2017, vol. 141, pp. 333-343. [ DOI : 10.1007/978-3-319-59448-4_32 ]

https://hal.inria.fr/hal-01533371
41K. Mcleod, M. Sermesant, X. Pennec.

Improving Understanding of Long-Term Cardiac Functional Remodelling via Cross-Sectional Analysis of Polyaffine Motion Parameters, in: FIMH 2017 - 9th International Conference on Functional Imaging and Modeling of the Heart, Toronto, Canada, Lecture Notes in Computer Science, Springer, June 2017, vol. 10263, pp. 51 - 59. [ DOI : 10.1007/978-3-319-59448-4_6 ]

https://hal.inria.fr/hal-01574837
42R. Molléro, H. Delingette, M. Datar, T. Heimann, J. Hauser, D. Panesar, A. Jones, A. Taylor, M. Kelm, T. Kuehne, M. Chinali, G. Rinelli, N. Ayache, X. Pennec, M. Sermesant.

Longitudinal Analysis using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies, in: Medical Image Computing and Computer Assisted Intervention (MICCAI), Québec City, Canada, Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, Springer International Publishing, September 2017, pp. 350-358. [ DOI : 10.1007/978-3-319-66185-8_40 ]

https://hal.inria.fr/hal-01569735
43X. Pennec.

Sample-limited L p Barycentric Subspace Analysis on Constant Curvature Spaces, in: Geometric Sciences of Information (GSI 2017), Paris, France, Geometric Science of Information, Springer International Publishing, November 2017, pp. 20-28. [ DOI : 10.1007/978-3-319-68445-1_3 ]

https://hal.inria.fr/hal-01574895
44M.-M. Rohé, M. Datar, T. Heimann, M. Sermesant, X. Pennec.

SVF-Net: Learning Deformable Image Registration Using Shape Matching, in: MICCAI 2017 - the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, Québec, Canada, Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, Springer International Publishing, September 2017, pp. 266-274. [ DOI : 10.1007/978-3-319-66182-7_31 ]

https://hal.inria.fr/hal-01557417
45M.-M. Rohé, M. Sermesant, X. Pennec.

Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net, in: Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop, Québec, Canada, September 2017.

https://hal.inria.fr/hal-01575297

Scientific Books (or Scientific Book chapters)

46S. Marchesseau, S. Chatelin, H. Delingette.

Non linear Biomechanical Model of the Liver: Hyperelastic Constitutive Laws for Finite Element Modeling, in: Biomechanics of Living Organs, Y. Payan, J. Ohayon (editors), Elsevier, June 2017, 602 p.

https://hal.inria.fr/hal-01536406

Scientific Popularization

47N. Miolane.

Les maths de l’espace-temps qui décrivent et dépassent le cerveau, in: Interstices, February 2017.

https://hal.inria.fr/hal-01503822

Patents

48H. Cochet, P. Jaïs, M. Sermesant.

Méthode de segmentation d’une image tridimensionnelle pour la génération d’un modèle de paroi du myocarde pour la détection d’au moins une zone de circulation électrique singulière, May 2017, n^o FR1754107.

https://hal.inria.fr/hal-01576064

Other Publications

49N. Miolane, S. Holmes, X. Pennec.

Topologically constrained template estimation via Morse-Smale complexes controls its statistical consistency, December 2017, working paper or preprint.

https://hal.inria.fr/hal-01655366
50A. Schmutz, J. Jacques, C. Bouveyron, L. Cheze, P. Martin.

Clustering multivariate functional data in group-specific functional subspaces, November 2017, working paper or preprint.

https://hal.inria.fr/hal-01652467

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Imaging cerebral atrophy: normal ageing to Alzheimer's disease, in: Lancet, 2004, vol. 363, n^o 9406.
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