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Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, p.384-394

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Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

IEEE transactions on medical imaging, 2021-12, Vol.40 (12), p.3543-3554

2021

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A Multi-view Crossover Attention U-Net Cascade with Fourier Domain Adaptation for Multi-domain Cardiac MRI Segmentation

Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge, p.323-334

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A 2-Step Deep Learning Method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation

Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, p.196-207

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Mesh U-Nets for 3D Cardiac Deformation Modeling

Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, p.245-257

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Post-Infarction Risk Prediction with Mesh Classification Networks

Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, p.291-301

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Interpretable cardiac anatomy modeling using variational mesh autoencoders

Understanding and Improving Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis

Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment

SMOD - Data Augmentation Based on Statistical Models of Deformation to Enhance Segmentation in 2D Cine Cardiac MRI

Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action

Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation

The 'Digital Twin' to enable the vision of precision cardiology

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

A Multi-view Crossover Attention U-Net Cascade with Fourier Domain Adaptation for Multi-domain Cardiac MRI Segmentation

A 2-Step Deep Learning Method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation

Mesh U-Nets for 3D Cardiac Deformation Modeling

Post-Infarction Risk Prediction with Mesh Classification Networks

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