Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges

Table of Contents

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   Book Overview
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   Chapter 1 Co-registered Cardiac ex vivo DT Images and Histological Images for Fibrosis Quantification
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   Chapter 2 Manufacturing of Ultrasound- and MRI-Compatible Aortic Valves Using 3D Printing for Analysis and Simulation
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   Chapter 3 Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders
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   Chapter 4 Ultra-DenseNet for Low-Dose X-Ray Image Denoising in Cardiac Catheter-Based Procedures
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   Chapter 5 A Cascade Regression Model for Anatomical Landmark Detection
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   Chapter 6 Comparison of 2D Echocardiography and Cardiac Cine MRI in the Assessment of Regional Left Ventricular Wall Thickness
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   Chapter 7 Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks
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   Chapter 8 4D CNN for Semantic Segmentation of Cardiac Volumetric Sequences
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    Chapter 9 Two-Stage 2D CNN for Automatic Atrial Segmentation from LGE-MRIs
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    Chapter 10 3D Left Ventricular Segmentation from 2D Cardiac MR Images Using Spatial Context
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    Chapter 11 Towards Hyper-Reduction of Cardiac Models Using Poly-affine Transformations
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    Chapter 12 Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames
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    Chapter 13 Learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary Hypertension
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    Chapter 14 Multimodal Cardiac Segmentation Using Disentangled Representation Learning
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    Chapter 15 DeepLA: Automated Segmentation of Left Atrium from Interventional 3D Rotational Angiography Using CNN
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    Chapter 16 Non-invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-Driven or Model-Based?
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    Chapter 17 Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage
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    Chapter 18 End-to-end Cardiac Ultrasound Simulation for a Better Understanding of Image Quality
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    Chapter 19 Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI
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    Chapter 20 Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training
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    Chapter 21 Style Data Augmentation for Robust Segmentation of Multi-modality Cardiac MRI
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    Chapter 22 Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
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    Chapter 23 An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR Image
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    Chapter 24 Cardiac Segmentation of LGE MRI with Noisy Labels
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    Chapter 25 Pseudo-3D Network for Multi-sequence Cardiac MR Segmentation
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    Chapter 26 SK-Unet: An Improved U-Net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR
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    Chapter 27 Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network
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    Chapter 28 Deep Learning Based Multi-modal Cardiac MR Image Segmentation
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    Chapter 29 Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN
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    Chapter 30 Knowledge-Based Multi-sequence MR Segmentation via Deep Learning with a Hybrid U-Net++ Model
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    Chapter 31 Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
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    Chapter 32 Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
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    Chapter 33 A Two-Stage Fully Automatic Segmentation Scheme Using Both 2D and 3D U-Net for Multi-sequence Cardiac MR
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    Chapter 34 Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation
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    Chapter 35 Best (and Worst) Practices for Organizing a Challenge on Cardiac Biophysical Models During AI Summer: The CRT-EPiggy19 Challenge
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    Chapter 36 Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical Maps
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    Chapter 37 Prediction of CRT Response on Personalized Computer Models
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    Chapter 38 Eikonal Model Personalisation Using Invasive Data to Predict Cardiac Resynchronisation Therapy Electrophysiological Response
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    Chapter 39 Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs
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    Chapter 40 Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation
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    Chapter 41 Left Ventricular Parameter Regression from Deep Feature Maps of a Jointly Trained Segmentation CNN
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    Chapter 42 A Two-Stage Temporal-Like Fully Convolutional Network Framework for Left Ventricle Segmentation and Quantification on MR Images