S. Al-khatib, M. , S. Stevenson, W. G. Ackerman, M. J. Bryant et al., AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: Executive summary: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society, Heart Rhythm, vol.35, 2017.

P. Anversa, G. Olivetti, and J. M. Capasso, Cellular basis of ventricular remodeling after myocardial infarction, The American journal of cardiology, vol.68, issue.14, pp.7-16, 1991.

N. Cedilnik, J. Duchateau, F. Sacher, P. Jaïs, H. Cochet et al., Fully Automated Electrophysiological Model Personalisation Framework from CT Imaging, International Conference on Functional Imaging and Modeling of the Heart, pp.325-333, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02106609

J. J. Goldberger, M. E. Cain, and S. H. Hohnloser, American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society scientific statement on noninvasive risk stratification techniques for identifying patients at risk for sudden cardiac death: a scientific statement from the American Heart Association Council on, Clinical Cardiology Committee on Electrocardiography and Arrhythmias and Council on Epidemiology and Prevention. Journal of the American College of Cardiology, vol.52, issue.14, pp.1179-1199, 2008.

B. Jáuregui, D. Soto-iglesias, D. Penela, and J. Acosta, Follow-up after myocardial infarction to explore the stability of arrhythmogenic substrate: the FOOT-PRINT study, JACC: Clinical Electrophysiology, vol.6, issue.2, pp.207-218, 2020.

S. Jia, A. Despinasse, Z. Wang, H. Delingette, X. Pennec et al., Automatically segmenting the left atrium from cardiac images using successive 3D U-nets and a contour loss, International Workshop on Statistical Atlases and Computational Models of the Heart, pp.221-229, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01860285

M. Lorenzi, M. Filippone, G. B. Frisoni, and D. C. Alexander, Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease, NeuroImage, vol.190, pp.56-68, 2019.

C. A. Martin and P. R. Gajendragadkar, Scar Tissue: Never Too Old to Remodel, JACC: Clinical Electrophysiology, vol.6, issue.2, 2020.

R. Martin, P. Maury, C. Bisceglia, T. Wong, H. Estner et al., Characteristics of scar-related ventricular tachycardia circuits using ultra-high-density mapping: a multi-center study, Circulation: Arrhythmia and Electrophysiology, vol.11, issue.10, p.6569, 2018.

C. E. Rasmussen, Gaussian processes in machine learning, Summer School on Machine Learning, pp.63-71, 2003.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.

A. J. Yezzi and J. L. Prince, An Eulerian PDE approach for computing tissue thickness, IEEE transactions on medical imaging, vol.22, issue.10, pp.1332-1339, 2003.