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

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Table of Contents

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    Book Overview
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    Chapter 1 Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data
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    Chapter 2 Learning Correspondences of Cardiac Motion from Images Using Biomechanics-Informed Modeling
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    Chapter 3 Multi-modal Latent-Space Self-alignment for Super-Resolution Cardiac MR Segmentation
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    Chapter 4 Towards Real-Time Optimization of Left Atrial Appendage Occlusion Device Placement Through Physics-Informed Neural Networks
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    Chapter 5 Haemodynamic Changes in the Fetal Circulation Post-connection to an Artificial Placenta: A Computational Modelling Study
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    Chapter 6 Personalized Fast Electrophysiology Simulations to Evaluate Arrhythmogenicity of Ventricular Slow Conduction Channels
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    Chapter 7 Self-supervised Motion Descriptor for Cardiac Phase Detection in 4D CMR Based on Discrete Vector Field Estimations
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    Chapter 8 Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling
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    Chapter 9 Comparison of Semi- and Un-Supervised Domain Adaptation Methods for Whole-Heart Segmentation
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    Chapter 10 Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging
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    Chapter 11 An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallot
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    Chapter 12 Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal
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    Chapter 13 Improving Echocardiography Segmentation by Polar Transformation
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    Chapter 14 Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach
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    Chapter 15 Interpretable Prediction of Post-Infarct Ventricular Arrhythmia Using Graph Convolutional Network
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    Chapter 16 Unsupervised Echocardiography Registration Through Patch-Based MLPs and Transformers
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    Chapter 17 Sensitivity Analysis of Left Atrial Wall Modeling Approaches and Inlet/Outlet Boundary Conditions in Fluid Simulations to Predict Thrombus Formation
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    Chapter 18 APHYN-EP: Physics-Based Deep Learning Framework to Learn and Forecast Cardiac Electrophysiology Dynamics
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    Chapter 19 Unsupervised Machine Learning Exploration of Morphological and Haemodynamic Indices to Predict Thrombus Formation in the Left Atrial Appendage
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    Chapter 20 Geometrical Deep Learning for the Estimation of Residence Time in the Left Atria
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    Chapter 21 Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection
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    Chapter 22 A Systematic Study of Race and Sex Bias in CNN-Based Cardiac MR Segmentation
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    Chapter 23 Mesh U-Nets for 3D Cardiac Deformation Modeling
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    Chapter 24 Skeletal Model-Based Analysis of the Tricuspid Valve in Hypoplastic Left Heart Syndrome
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    Chapter 25 Simplifying Disease Staging Models into a Single Anatomical Axis - A Case Study of Aortic Coarctation In-utero
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    Chapter 26 Point2Mesh-Net: Combining Point Cloud and Mesh-Based Deep Learning for Cardiac Shape Reconstruction
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    Chapter 27 Post-Infarction Risk Prediction with Mesh Classification Networks
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    Chapter 28 Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries
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    Chapter 29 Computerized Analysis of the Human Heart to Guide Targeted Treatment of Atrial Fibrillation
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    Chapter 30 3D Mitral Valve Surface Reconstruction from 3D TEE via Graph Neural Networks
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    Chapter 31 Efficient MRI Reconstruction with Reinforcement Learning for Automatic Acquisition Stopping
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    Chapter 32 Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
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    Chapter 33 PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images
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    Chapter 34 Deep Computational Model for the Inference of Ventricular Activation Properties
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    Chapter 35 Semi-supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels
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    Chapter 36 Cardiac Segmentation Using Transfer Learning Under Respiratory Motion Artifacts
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    Chapter 37 Deep Learning Based Classification and Segmentation for Cardiac Magnetic Resonance Imaging with Respiratory Motion Artifacts
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    Chapter 38 Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation
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    Chapter 39 Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts Using Multi-task Learning and K-Space Motion Artefact Augmentation
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    Chapter 40 Motion-Related Artefact Classification Using Patch-Based Ensemble and Transfer Learning in Cardiac MRI
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    Chapter 41 Automatic Image Quality Assessment and Cardiac Segmentation Based on CMR Images
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    Chapter 42 Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation
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    Chapter 43 3D MRI Cardiac Segmentation Under Respiratory Motion Artifacts
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    Chapter 44 Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifacts Using Simulated Data
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    Chapter 45 Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification
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    Chapter 46 Automatic Cardiac Magnetic Resonance Respiratory Motions Assessment and Segmentation
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    Chapter 47 Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation
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    Chapter 48 A Deep Learning-Based Fully Automatic Framework for Motion-Existing Cine Image Quality Control and Quantitative Analysis
Attention for Chapter 44: Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifacts Using Simulated Data
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Chapter title
Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifacts Using Simulated Data
Chapter number 44
Book title
Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-23443-9_44
Book ISBNs
978-3-03-123442-2, 978-3-03-123443-9
Authors

Amirrajab, Sina, Al Khalil, Yasmina, Pluim, Josien, Breeuwer, Marcel, Scannell, Cian M.

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Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 50%
Student > Ph. D. Student 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Engineering 1 50%