This book constitutes the proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th MICCAI conference. The 34 regular workshop papers included in this volume were carefully reviewed and selected after being revised and deal with topics such as: common cardiac segmentation and modelling problems to more advanced generative modelling for ageing hearts, learning cardiac motion using biomechanical networks, physics-informed neural networks for left atrial appendage occlusion, biventricular mechanics for Tetralogy of Fallot, ventricular arrhythmia prediction by using graph convolutional network, and deeper analysis of racial and sex biases from machine learning-based cardiac segmentation. In addition, 14 papers from the CMRxMotion challenge are included in the proceedings which aim to assess the effects of respiratory motion on cardiac MRI (CMR) imaging quality and examine the robustness of segmentation models in face of respiratory motion artefacts. A total of 48 submissions to the workshop was received.

Table of Contents:
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.