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

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Modelling Cardiac Motion via Spatio-Temporal Graph Convolutional Networks to Boost the Diagnosis of cardiac MR cine images for improving the characterization of heart conditions. Specifically, a novel GCN architecture is used, where the sample nodes of endocardial and epicardial contours are connected as a graph to represent the myocardial geometry. We show that the ST-GCN can automatically quant

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PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Mapsties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art card

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Quality-Aware Semi-supervised Learning for CMR Segmentationis limitation, data augmentation and semi-supervised learning (SSL) methods have been developed. However, these methods have limited effectiveness as they either exploit the existing data set only (data augmentation) or risk negative impact by adding poor training examples (SSL). Segmentations are r

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Estimation of Imaging Biomarker’s Progression in Post-infarct Patients Using Cross-sectional Datahythmia several years after the infarct event suggesting that scar remodeling is a process that might require years until the affected tissue becomes arrhythmogenic. In clinical practice, a simple time-based rule is often used to assess risk and stratify patients. In other cases, left ventricular ej

Acetabulum

PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Datanvasive procedure navigation. Many cardiac image segmentation methods have relied on detection of region-of-interest as a pre-requisite for shape segmentation and modeling. With segmentation results, a 3D surface mesh and a corresponding point cloud of the segmented cardiac volume can be reconstruct

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Left Atrial Ejection Fraction Estimation Using SEGANet for Fully Automated Segmentation of CINE MRIreatments for AF are often ineffective and few atrial biomarkers exist to automatically characterise atrial function and aid in treatment selection for AF. Clinical metrics of left atrial (LA) function, such as ejection fraction (EF) and active atrial contraction ejection fraction (aEF), are promisi