Titlebook: Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges; 11th International W Esther Puyol Anton,Mihaela Pop,

内疚

https://doi.org/10.1007/978-3-030-68107-4artificial intelligence; cardiac imaging; computer modelling; computer vision; CT; deep learning; electro-

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CON

Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges978-3-030-68107-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

FOLD

Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networksthout iterative solvers or manual correction, which is . faster than the mathematical model. We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.

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Glower

PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps named ., performed robustly with synthetic data and showed promising results with clinical data. These results suggest that . could be a useful supplement in future clinical workflowss of personalized therapies.

Substitution

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hegemony

Cholagogue

A Cartesian Grid Representation of Left Atrial Appendages for a Deep Learning Estimation of Thrombogs built from patient-specific data. Some deep learning architectures, such as Fully Connected Networks (FCN), have demonstrated potential in accelerating CFD simulations, determining the relation between object geometry and model outcomes after finding correspondences with classical surface registra