Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers

left atria.- Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection.- A systematic study of race and sex bias in CNN-based cardiac MR segmentation.- Mesh U-Nets for 3D Cardiac Deformation Modeling.- Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.- Simplifying Disease Staging Models into a Single Anatomical Axis - A Case Study of Aortic Coarctation In-utero.- Point2Mesh-Net: Combining Point Cloud and Mesh-Based Deep Learning for Cardiac Shape Reconstruction.- Post-Infarction Risk Prediction with Mesh Classification Networks.- Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries.- Computerized Analysis of the Human Heart to Guide Targeted Treatment of Atrial Fibrillation.- 3D Mitral Valve Surface Reconstruction from 3D TEE via Graph Neural Networks.- Efficient MRI Reconstruction with Reinforcement Learning for Automatic Acquisition Stopping.- Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels.- PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images.- Deep Computational Model for the Inference of Ventricular Activation Properties.- Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels.- Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts.- Deep Learning Based Classification and Segmentation for Cardiac Magnetic Resonance Imaging with Respiratory Motion Artifacts.- Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation.- Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts using Multi-task Learning and K-Space Motion Artefact Augmentation.- Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI.- Automatic Image Quality Assessment and Cardiac Segmentation Based on CMR Images.- Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation.- 3D MRI cardiac segmentation under respiratory motion artifacts.- Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifact using Simulated Data.- Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification.- Automatic Cardiac Magnetic Resonance Respiratory Motions Assessment and Segmentation.- Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation.- A deep learning-based fully automatic framework for motion-existing cine image quality control and quantitative analysis.