B. Avants, C. Epstein, M. Grossman, and J. Gee, Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain, Med Image Anal, vol.12, issue.1, pp.26-41, 2008.

W. Bai, O. Oktay, M. Sinclair, M. Suzuki, M. Rajchl et al., Semi-supervised learning for network-based cardiac MR image segmentation, vol.MICCAI, pp.253-260, 2017.

G. Balakrishnan, A. Zhao, M. Sabuncu, J. Guttag, and A. Dalca, VoxelMorph: a learning framework for deformable medical image registration, 2018.

O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang et al., Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved, IEEE Trans Med Imaging, vol.37, issue.11, pp.2514-2525, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01803621

C. Bouveyron, S. Girard, and C. Schmid, High dimensional data clustering, Computational Statistics and Data Analysis, vol.52, pp.502-519, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00548573

M. Cerqueira, N. Weissman, V. Dilsizian, A. Jacobs, S. Kaul et al., Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: A statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the american heart association, 2002.

I. Cetin, G. Sanroma, S. Petersen, S. Napel, O. Camara et al., A radiomics approach to computer-aided diagnosis with cardiac cine-MRI, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, 2017.

V. Cheplygina, M. De-bruijne, and J. Pluim, Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis, 2018.

D. Comaniciu, K. Engel, B. Georgescu, and T. Mansi, Shaping the future through innovations: from medical imaging to precision medicine, Med Image Anal, vol.33, pp.19-26, 2016.

T. Dawes, A. De-marvao, W. Shi, T. Fletcher, G. Watson et al., Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study, Radiology, vol.283, pp.381-390, 2017.

B. Gao, W. Liu, L. Wang, P. Liu, P. Croisille et al., Estimation of cardiac motion in cine-MRI sequences by correlation transform optical flow of monogenic features distance, Phys Med Biol, vol.61, pp.8640-8663, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01434871

K. Gilbert, B. Pontre, C. Occleshaw, B. Cowan, A. Suinesiaputra et al., 4D modelling for rapid assessment of biventricular function in congenital heart disease, The International Journal of Cardiovascular Imaging, vol.34, issue.3, pp.407-417, 2017.

L. Gu, Y. Zheng, R. Bise, I. Sato, N. Imanishi et al., Semisupervised learning for biomedical image segmentation via forest oriented super pixels(voxels). MICCAI, pp.702-710, 2017.

A. Hering, S. Kuckertz, S. Heldmann, and M. Heinrich, Enhancing labeldriven deep deformable image registration with local distance metrics for state-of-the-art cardiac motion tracking, Bildverarbeitung für die Medizin, pp.309-314, 2019.

M. Hernandez, S. Olmos, and X. Pennec, Comparing algorithms for diffeomorphic registration: stationary LDDMM and diffeomorphic demons, Proc. MFCA, pp.24-35, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00629883

A. Holzinger, C. Biemann, C. Pattichis, and D. Kell, What do we need to build explainable AI systems for the medical domain, 2017.

F. Isensee, P. Jaeger, P. Full, I. Wolf, S. Engelhardt et al., Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, 2017.

M. Khened, V. Alex, and G. Krishnamurthi, Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, 2017.

M. Khened, V. Alex, and G. Krishnamurthi, Fully convolutional multiscale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers, 2018.

J. Krebs, H. Delingette, B. Mailhé, N. Ayache, and T. Mansi, Learning a probabilistic model for diffeomorphic registration, IEEE Transactions on Medical Imaging, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01978339

J. Krebs, T. Mansi, B. Mailhé, N. Ayache, and H. Delingette, Unsupervised probabilistic deformation modeling for robust diffeomorphic registration, Proc. Deep Learning in Medical Image Analysis (DLMIA), MICCAI'18 Workshop, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01845688

H. Li and Y. Fan, Non-rigid image registration using fully convolutional networks with deep self-supervision, 2017.

M. Lorenzi, N. Ayache, G. Frisoni, and X. Pennec, LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm, NeuroImage, vol.81, pp.470-483, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00819895

A. Lu, N. Parajuli, M. Zontak, J. Stendahl, K. Ta et al., Learning-based regularization for cardiac strain analysis with ability for domain adaptation, 2018.

F. Orlhac, P. Mattei, C. Bouveyron, and N. Ayache, Class-specific variable selection in high-dimensional discriminant analysis through bayesian sparsity, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01811514

N. Parajuli, A. Lu, J. Stendahl, M. Zontak, N. Boutagy et al., Flow network based cardiac motion tracking leveraging learned feature matching, pp.279-286, 2017.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikitlearn: machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

X. Pennec, P. Cachier, and N. Ayache, Understanding the "demon's algorithm": 3D non-rigid registration by gradient descent. MICCAI'99, pp.597-605, 1999.

S. Petersen, P. Matthews, J. Francis, M. Robson, F. Zemrak et al., UK Biobank's cardiovascular magnetic resonance protocol, J Cardiovasc Magn Reson, vol.18, issue.8, p.8, 2016.

C. Qin, W. Bai, J. Schlemper, S. Petersen, S. Piechnik et al., Joint learning of motion estimation and segmentation for cardiac MR image sequences, pp.472-480, 2018.

C. Qin, W. Bai, J. Schlemper, S. Petersen, S. Piechnik et al., Joint motion estimation and segmentation from undersampled cardiac MR image, pp.55-63, 2018.

P. Radau, Y. Lu, K. Connelly, G. Paul, A. Dick et al., Evaluation framework for algorithms segmenting short axis cardiac MRI, The MIDAS Journal -Cardiac MR Left Ventricle Segmentation Challenge, 2009.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, MICCAI, vol.9351, pp.234-241, 2015.

D. Rueckert, B. Glocker, and B. Kainz, Learning clinically useful information from images: past, present and future, Medical Image Analysis, vol.33, pp.13-18, 2016.

J. Rühaak, S. Heldmann, T. Kipshagen, and B. Fischer, Highly accurate fast lung CT registration, Image Processing, vol.8669, 2013.

A. Suinesiaputra, P. Ablin, X. Alba, M. Alessandrini, J. Allen et al., Statistical shape modeling of the left ventricle: myocardial infarct classification challenge, IEEE J Biomed Health Inform, vol.22, issue.2, pp.503-515, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01533805

A. Suinesiaputra, A. Mcculloch, M. Nash, B. Pontre, and A. Young, Cardiac image modelling: breadth and depth in heart disease, Medical Image Analysis, vol.33, pp.38-43, 2016.
DOI : 10.1016/j.media.2016.06.027

URL : http://europepmc.org/articles/pmc5123588?pdf=render

M. Tipping and A. Faul, Fast marginal likelihood maximisation for sparse bayesian models, 2003.

B. De-vos, F. Berendsen, M. Viergever, M. Staring, and I. Isgum, End-toend unsupervised deformable image registration with a convolutional neural network. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, Conjunction with MICCAI 2017, pp.204-212, 2017.

J. Weese and C. Lorenz, Four challenges in medical image analysis from an industrial perspective, Medical Image Analysis, vol.33, pp.44-49, 2016.
DOI : 10.1016/j.media.2016.06.023

J. Wolterink, T. Leiner, M. Viergever, and I. Isgum, Automatic segmentation and disease classification using cardiac cine MR images, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, 2017.
DOI : 10.1007/978-3-319-75541-0_11

URL : http://arxiv.org/pdf/1708.01141

W. Xue, G. Brahm, S. Pandey, S. Leung, and S. Li, Full left ventricle quantification via deep multitask relationships learning, Med Image Anal, vol.43, pp.54-65, 2018.
DOI : 10.1016/j.media.2017.09.005

W. Yan, Y. Wang, Z. Li, R. Van-der-geest, and Q. Tao, Left ventricle segmentation via optical-flow-net from short-axis cine MRI: preserving the temporal coherence of cardiac motion, pp.613-621, 2018.

D. Yang, P. Wu, C. Tan, K. Pohl, L. Axel et al., 3D motion modeling and reconstruction of left ventricle wall in cardiac MRI, ternational Conference on Functional Imaging and Modeling of the Heart, pp.481-492, 2017.

Q. Zheng, H. Delingette, N. Duchateau, and N. Ayache, 3D consistent and robust segmentation of cardiac images by deep learning with spatial propagation, IEEE Trans Med Imaging, vol.37, issue.9, pp.2137-2148, 2018.