E-raamat: Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge: 12th International Workshop, STACOM 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Revised Selected Papers

Multi-atlas segmentation of the aorta from 4D flow MRI: comparison of
several fusion strategie.- Quality-aware Cine Cardiac MRI Reconstruction and
Analysis from Undersampled k-space Data.- Coronary Artery Centerline
Refinement using GCN Trained with Synthetic Data.- Novel imaging biomarkers
to evaluate heart dysfunction post-chemotherapy: a preclinical MRI
feasibility study.- A bi-atrial statistical shape model as a basis to
classify left atrial enlargement from simulated and clinical 12-lead ECGs.-
Vessel Extraction and Analysis of Aortic Dissection.- The Impact of Domain
Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR
Images.- Characterizing myocardial ischemia and reperfusion patterns with
hierarchical manifold learning.- Generating Subpopulation-Specific
Biventricular Anatomy Models Using Conditional Point Cloud Variational
Autoencoders.- Improved AI-based Segmentation of Apical and Basal Slices from
Clinical Cine CMR.- Mesh Convolutional Neural Networks forWall Shear Stress
Estimation in 3D Artery Models.- Hierarchical multi-modality prediction model
to assess obesity-related remodelling.- Neural Angular Plaque
Characterization:Automated Quantification of Polar Distributionfor Plaque
Composition.- Simultaneous Segmentation and Motion Estimation of Left
Ventricular Myocardium in 3D Echocardiography using Multi-task Learning.-
Statistical shape analysis of the tricuspid valve in hypoplastic left heart
syndrome.- An Unsupervised 3D Recurrent Neural Networkfor Slice Misalignment
Correction in CardiacMR Imaging.- Unsupervised Multi-Modality
RegistrationNetwork based on Spatially Encoded Gradient Information.-
In-silico analysis of device-related thrombosis for different left atrial
appendage occluder settings.- Valve flattening with functional biomarkers for
the assessment of mitral valve repair.- Multi-modality cardiac segmentation
via mixing domains for unsupervised adaptation.- Uncertainty-Aware Training
for Cardiac Resynchronisation Therapy Response Prediction.- Cross-domain
Artefact Correction of Cardiac MRI.- Detection and Classification of Coronary
Artery Plaques in Coronary Computed Tomography Angiography Using 3D CNN.-
Predicting 3D Cardiac Deformations With Point Cloud Autoencoders.- Inuence
of morphometric and mechanical factors in thoracic aorta nite element
modeling.- Right Ventricle Segmentation via Registration and Multi-input
Modalities in Cardiac Magnetic Resonance Imaging from Multi-Disease,
Multi-View and Multi-Center.- Using MRI-specific Data Augmentation to Enhance
the Segmentation of Right Ventricle in Multi-disease, Multi-center and
Multi-view  Cardiac MRI.- Right Ventricular Segmentation from Short- and
Long-Axis MRIs via Information Transition.- Tempera: Spatial Transformer
Feature Pyramid Network for Cardiac MRI Segmentation.- Multi-view SA-LA Net:
A framework for simultaneous segmentation of RV on multi-view cardiac MR
Images.- Right ventricular segmentation in multi-view cardiac MRI using a
unified U-net model.- Deformable Bayesian Convolutional Networks for
Disease-Robust Cardiac MRI Segmentation.- Consistency based Co-Segmentation
for Multi-View Cardiac MRI using Vision Transformer.- Refined Deep Layer
Aggregation for Multi-Disease, Multi-View & Multi-Center Cardiac MR
Segmentation.- A Multi-View Cross-Over Attention U-Net Cascade With Fourier
Domain Adaptation For Multi-Domain Cardiac MRI Segmentation.- Multi-Disease,
Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI using
Efficient Late-Ensemble Deep Learning Approach.- Automated Segmentation of
the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional
Neural Networks.- 3D right ventricle reconstruction from 2D U-Net
segmentation of sparse short-axis and 4-chamber cardiac cine MRI views.- Late
Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI
Segmentation.- Using Out-of-Distribution Detection for Model Refinement in
Cardiac Image Segmentation.