E-raamat: Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers: 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers

Generative Modelling of the Ageing Heart with Cross-Sectional Imaging
and Clinical Data.- Learning correspondences of cardiac motion using
biomechanics-informed modeling.- Multi-modal Latent-space Self-alignment for
Super-resolution Cardiac MR Segmentation.- Towards real-time optimization of
left atrial appendage occlusion device placement through physics-informed
neural networks.- Haemodynamic changes in the fetal circulation after
connection to an artificial placenta: a computational modelling study.-
Personalized Fast Electrophysiology Simulations to Evaluate Arrhythmogenicity
of Ventricular Slow Conduction Channels.- Self-supervised motion descriptor
for cardiac phase detection in 4D CMR based on discrete vector field
estimations.- Going Off-Grid: Continuous Implicit Neural Representations for
3D Vascular Modeling.- Comparison of Semi- and Un-supervised Domain
Adaptation Methods for Whole-Heart Segmentation.- Automated Quality
Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance
Imaging.- An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of
Fallot.- Review of data types and model dimensionality for cardiac DTI
SMS-related artefact removal.- Improving Echocardiography Segmentation by
Polar Transformation.- Spatiotemporal Cardiac Statistical Shape Modeling: A
Data-Driven Approach.- Interpretable Prediction of Post-Infarct Ventricular
Arrhythmia using Graph Convolutional Network.- Unsupervised Echocardiography
Registration through Patch-based MLPs and Transformers.- Sensitivity analysis
of left atrial wall modeling approaches and inlet/outlet boundary conditions
in fluid simulations to predict thrombus formation.- APHYN-EP: Physics-based
deep learning framework to learn and forecast cardiac electrophysiology
dynamics.- Unsupervised machine-learning exploration of morphological and
haemodynamic indices to predict thrombus formation at the left atrial
appendage.- Geometrical deep learning for the estimation of residence time
inthe 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.