,
, 103 6.1.2 Consistent and Robust Segmentation with Spatial Propagation 104
, Explainable Pathology Classification with Motion Characterization
, Cluster Analysis of Image-Derived Features
,
105 6.4.1 Cardiac Mesh Simulation and Image Synthesis for Deep Learning105 6.4.2 Temporal Consistency of Segmentation ,
103 6.1.2 Consistent and Robust Segmentation with Spatial Propagation 104 6.1.3 Explainable Pathology Classification with Motion Characterization, Contents 6.1 Main Contributions ,
Cluster Analysis of Image-Derived Features, vol.104 ,
,
105 6.4.1 Cardiac Mesh Simulation and Image Synthesis for Deep Learning105 6.4.2 Temporal Consistency of Segmentation ,
, we explored deep learning for robust segmentation and explainable analysis of 3D and dynamic cardiac images. Now we summarize the main contributions and discuss some perspectives
Caregiver burden: a clinical review, JAMA, vol.311, pp.1052-1060, 2014. ,
Unsupervised medical image segmentation based on the local center of mass, Scientific Reports, vol.8, p.107, 2018. ,
Addressing the physicians' shortage in developing countries by accelerating and reforming the medical education: is it possible?, J Adv Med Educ Prof, vol.5, issue.4, pp.209-212, 2017. ,
High throughput computation of reference ranges of biventricular cardiac function on the UK Biobank population cohort, vol.84, 2019. ,
Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain, Med Image Anal, vol.12, issue.1, p.73, 2008. ,
A combined deeplearning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI, Med Image Anal, vol.30, p.41, 2016. ,
Fully automatic segmentation of heart chambers in cardiac MRI using deep learning, J Cardiovasc Magn Reson, vol.18, p.45, 2016. ,
Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach, Magn. Reson. Med, vol.78, issue.6, pp.2439-2448, 2017. ,
Semi-supervised learning for networkbased cardiac MR image segmentation. MICCAI, vol.107, pp.253-260, 2017. ,
Human-level CMR image analysis with deep fully Bibliography convolutional networks, vol.45, 2017. ,
VoxelMorph: a Learning framework for deformable medical image registration, p.56, 2018. ,
Imaging biomarkers in multiple sclerosis: from image analysis to population imaging, Medical Image Analysis, vol.33, pp.134-139, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01333583
An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, p.18, 2017. ,
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
High dimensional data clustering, Computational Statistics and Data Analysis, vol.52, p.77, 2007. ,
URL : https://hal.archives-ouvertes.fr/inria-00548591
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, p.58, 2002. ,
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. ,
Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation, Advances in Neural Information Processing Systems, pp.3036-3044, 2016. ,
Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans, Nat Sci Rep, vol.6, p.24454, 2016. ,
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis, vol.107, 2018. ,
Shaping the future through innovations: from medical imaging to precision medicine, Med Image Anal, vol.33, pp.19-26, 2016. ,
Using deep learning to segment breast and fibroglandular tissue in MRI volumes, Medical physics, vol.44, issue.2, pp.533-546, 2017. ,
Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study, Radiology, vol.283, p.51, 2017. ,
Machine learning approaches in medical image analysis: from detection to diagnosis, Medical Image Analysis, vol.33, pp.94-97, 2016. ,
End-toend unsupervised deformable image registration with a convolutional neural network. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, vol.10553, p.56, 2017. ,
Infarct localization from myocardial deformation: Prediction and uncertainty quantification by regression from a low-dimensional space, IEEE Trans. Med. Imaging, vol.35, issue.10, pp.2340-2352, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01314767
Modelbased generation of large databases of cardiac images: synthesis of pathological cine MR sequences from real healthy cases, IEEE Trans. Med. Imaging, vol.37, p.106, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01533788
Automatic Bibliography model-based segmentation of the heart in CT images, IEEE Trans. Med. Imaging, vol.27, issue.9, pp.1189-2201, 2008. ,
Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data, J Digit Imaging, vol.30, issue.4, pp.406-412, 2017. ,
Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population, Am J Epidemiol, vol.186, issue.9, pp.1026-1034, 2017. ,
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
4D modelling for rapid assessment of biventricular function in congenital heart disease, The International Journal of Cardiovascular Imaging, vol.34, issue.3, p.51, 2017. ,
Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels). MICCAI, vol.50, pp.702-710, 2017. ,
HEp-2 cell classification using K-support spatial pooling in deep, CNNs. DLMIA, vol.10008, pp.3-11, 2016. ,
Brain age prediction of healthy subjects on anatomic MRI with deep learning: going beyond with an "explainable AI" mindset. bioRxiv preprint 413302, p.107, 2018. ,
Enhancing labeldriven deep deformable image registration with local distance metrics for state-of-the-art cardiac motion tracking. Bildverarbeitung für die Medizin, p.74, 2019. ,
Comparing algorithms for diffeomorphic registration: stationary LDDMM and diffeomorphic demons, Proc. MFCA, p.56, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00629883
What do we need to build explainable AI systems for the medical domain?, p.107, 2017. ,
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, p.17 ,
Automatic segmentation of LV and RV in cardiac MRI, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, vol.40, 2017. ,
Automatic segmentation of the myocardium in cine MR images using deformable registration. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, vol.85, pp.98-108, 2013. ,
Clustering techniques for brain tumor detection, Proc. of Int. Conf. on Recent Trends in Information, vol.84, pp.299-305, 2014. ,
CNN-based segmentation of medical imaging data, p.26, 2017. ,
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, MIC-CAI'17 Workshop, 2017. ,
Fully convolutional multiscale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers, 2018. ,
Medical imaging lesion detection based on unified gravitational fuzzy clustering, Journal of Healthcare Engineering, vol.2017, 2017. ,
Medical image data and datasets in the era of machine learning-whitepaper from the 2016 C-MIMI meeting dataset session, Journal of Digital Imaging, vol.30, p.97, 2017. ,
Machine learning methods for histopathological image analysis, Computational and Structural Biotechnology, vol.16, pp.34-42, 2018. ,
Unsupervised probabilistic deformation modeling for robust diffeomorphic registration, Proc. Deep Learning in Medical Image Analysis (DLMIA), MICCAI'18 Workshop, p.56, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01845688
Learning a probabilistic model for diffeomorphic registration, IEEE Transactions on Medical Imaging, p.73, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01978339
Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach, Artificial Intelligence in Medicine, vol.94, p.107, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01998052
Deep learning, Nature, vol.521, pp.436-444, 2015. ,
Medical big data: promise and challenges, Kidney Res Clin Pract, vol.36, issue.1, pp.3-11, 2017. ,
Non-rigid image registration using fully convolutional networks with deep self-supervision, p.56, 2017. ,
A survey on deep learning in medical image analysis, Med Image Anal, vol.42, pp.60-88, 2017. ,
Anatomical landmark based deep feature representation for MR images in brain disease diagnosis, IEEE Journal of Biomedical and Health Informatics, vol.22, pp.1476-1485, 2018. ,
LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm, Neu-roImage, vol.81, p.73, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00819895
Cardiac anchoring in MRI through context modeling, Med Image Comput Comput Assist Interv, vol.13, issue.1, pp.383-390, 2010. ,
Learning-based regularization for cardiac strain analysis with ability for domain adaptation, p.51, 2018. ,
Rectifier nonlinearities improve neural network acoustic models, Proc. ICML, vol.30, p.26, 2013. ,
Image analysis and machine learning in digital pathology: Challenges and opportunities, Med Image Anal, vol.33, pp.170-175, 2017. ,
Threshold selection for classification of MR brain images by clustering method, AIP Conference Proceedings, vol.1694, 2015. ,
Unsupervised segmentation of 3D medical images based on clustering and deep representation learning, Proceedings of the SPIE, vol.10578, 2018. ,
Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation, IEEE Trans Med Imaging, vol.37, pp.384-395, 2018. ,
Class-specific variable selection in high-dimensional discriminant analysis through bayesian sparsity, p.77, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01811514
Flow network based cardiac motion tracking leveraging learned feature matching. MICCAI, vol.50, pp.279-286, 2017. ,
Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer's disease, Medical Image Analysis, vol.48, pp.117-130, 2018. ,
Scikitlearn: machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Understanding the "demon's algorithm": 3D non-rigid registration by gradient descent. MICCAI'99, p.56, 1999. ,
UK Biobank's cardiovascular magnetic resonance protocol, J Cardiovasc Magn Reson, vol.18, issue.8, 2016. ,
Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort, J Cardiovasc Magn Reson, vol.19, issue.1, p.83, 2017. ,
Right ventricle segmentation from cardiac MRI: A collation study, Medical Image Analysis, vol.19, issue.1, p.22, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01081337
Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation, p.41, 2016. ,
Generation of synthetic but visually realistic time series of cardiac images combining a biophysical model and clinical images, IEEE Trans. Med. Imaging, vol.32, issue.1, p.12, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00813861
Joint learning of motion estimation and segmentation for cardiac MR image sequences. MICCAI, p.51, 2018. ,
Joint motion estimation and segmentation from undersampled cardiac MR image, p.51, 2018. ,
Fast automatic myocardial segmentation in 4D cine CMR datasets, Med Image Anal, vol.18, p.41, 2014. ,
Evaluation framework for algorithms segmenting short axis cardiac MRI, The MIDAS Journal -Cardiac MR Left Ventricle Segmentation Challenge, 2009. ,
Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning, IEEE Int Symp Biomedical Imaging, pp.779-782, 2016. ,
A tour of unsupervised deep learning for medical image analysis, vol.107, 2018. ,
Detecting novel associations in large data sets, Science, vol.334, pp.1518-1524, 2011. ,
Gaussian mixture models. Encyclopedia of Biometrics, vol.87, pp.659-663, 2009. ,
U-net: Convolutional networks for biomedical image segmentation. MICCAI, vol.9351, pp.234-241, 2015. ,
Learning clinically useful information from images: past, present and future, Medical Image Analysis, vol.33, pp.13-18, 2016. ,
Highly accurate fast lung CT registration, Image Processing, vol.8669, p.74, 2013. ,
Standardized image interpretation and post processing in cardiovascular magnetic resonance: society for cardiovascular magnetic resonance (SCMR) board of trustees task force on standardized post processing, J Cardiovasc Magn Reson, vol.15, issue.35, p.23, 2013. ,
Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours, Journal of Cardiovascular Magnetic Resonance, vol.17, issue.1, pp.38-43, 2015. ,
Fullyautomated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results, Int J Cardiovasc Imaging, vol.34, issue.2, pp.281-291, 2018. ,
Fast marginal likelihood maximisation for sparse bayesian models, AISTATS, vol.77, 2003. ,
Benchmarking framework for myocardial tracking and deformation algorithms: an open access database, Med Image Anal, vol.17, issue.6, pp.632-648, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00855928
The Alzheimer's disease neuroimaging initiative informatics core: a decade in review, Alzheimers Dement, vol.11, pp.832-839, 2015. ,
A fully convolutional neural network for cardiac segmentation in short-axis MRI, 2016. ,
Visualizing data using t-sne, J. Mach. Learn. Research, vol.9, p.93, 2008. ,
Learning-based detection and tracking in medical imaging: A probabilistic approach, Deformation Models, vol.7, pp.209-235, 2013. ,
Four challenges in medical image analysis from an industrial perspective, Medical Image Analysis, vol.33, pp.44-49, 2016. ,
, Deep learning for generalized biventricular cardiac mass and function parameters, 2017.
All models are wrong ...': an introduction to model uncertainty, Statistica Neerlandica, vol.66, p.88, 2012. ,
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. ,
Full left ventricle quantification via deep multitask relationships learning, Med Image Anal, vol.43, p.51, 2018. ,
Left ventricle segmentation via optical-flow-net from short-axis cine MRI: preserving the temporal coherence of cardiac motion. MICCAI, p.51, 2018. ,
3D motion modeling and reconstruction of left ventricle wall in cardiac MRI, International Conference on Functional Imaging and Modeling of the Heart, p.51, 2017. ,
Large-scale medical image analytics: Recent methodologies, applications and future directions, Medical Image Analysis, vol.33, pp.98-101, 2016. ,
Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features, IEEE Trans Med Imaging, vol.27, pp.1668-1681, 2008. ,
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01975880
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. ,
3D consistent biventricular myocardial segmentation using deep learning for mesh generation, vol.87, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01755317
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank, vol.105, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02043380
Deep learning for medical image analysis, 2017. ,
Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation, Comput Med Imaging Graph, vol.55, pp.28-41, 2017. ,
Multi-atlas propagation whole heart segmentation from MRI and CTA using a local normalised correlation coefficient criterion. Functional Imaging and Modeling of the Heart, p.45, 2013. ,