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Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

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Cover of 'Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Open Science as-a-Service for Research Communities and Content Providers
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    Chapter 1 Estimating Sheets in the Heart Wall
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    Chapter 2 Automated Motion Correction and 3D Vessel Centerlines Reconstruction from Non-simultaneous Angiographic Projections
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    Chapter 2 Applying Auctions to Bank Holding Company Software Project Portfolio Selection
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    Chapter 3 Preventing Collusion in Cloud Computing Auctions
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    Chapter 3 Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning
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    Chapter 4 Statistical Shape Clustering of Left Atrial Appendages
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    Chapter 4 Why Are Repeated Auctions in RaaS Clouds Risky?
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    Chapter 5 An Evaluation of a Market Based Resource Trading in a Multi-campus Compute Co-operative (CCC)
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    Chapter 5 Deep Learning Segmentation of the Left Ventricle in Structural CMR: Towards a Fully Automatic Multi-scan Analysis
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    Chapter 6 Snooping Around a Fence: A Lesson from the Education Sector in a Software Service Ecosystem
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    Chapter 6 Cine and Multicontrast Late Enhanced MRI Registration for 3D Heart Model Construction
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    Chapter 7 Joint Analysis of Personalized In-Silico Haemodynamics and Shape Descriptors of the Left Atrial Appendage
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    Chapter 7 Model and Simulation Engines for Distributed Simulation of Discrete Event Systems
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    Chapter 8 How Accurately Does Transesophageal Echocardiography Identify the Mitral Valve?
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    Chapter 8 An HVAC Regulation Architecture for Smart Building Based on Weather Forecast
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    Chapter 9 Stochastic Model-Based Left Ventricle Segmentation in 3D Echocardiography Using Fractional Brownian Motion
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    Chapter 9 Statistical Model Based Cloud Resource Management
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    Chapter 10 Network Externalities in Cybersecurity Information Sharing Ecosystems
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    Chapter 10 Context Aware 3D Fully Convolutional Networks for Coronary Artery Segmentation
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    Chapter 11 AMFC Tool: Auditing and Monitoring for Cloud Computing
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    Chapter 11 Learning Associations Between Clinical Information and Motion-Based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas
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    Chapter 12 Business Model Characteristics for Local IaaS Providers for Counteracting the Dominance of the Hyperscalers
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    Chapter 12 Computational Modelling of Electro-Mechanical Coupling in the Atria and Its Changes During Atrial Fibrillation
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    Chapter 13 Delivering a Systematic Framework for the Selection and Evaluation of Startups
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    Chapter 13 High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort
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    Chapter 14 Lumen Segmentation of Aortic Dissection with Cascaded Convolutional Network
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    Chapter 14 Service User Perspectives on Delivering Social Innovation: An Implication of the Internet of Things for Business
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    Chapter 15 A Vessel-Focused 3D Convolutional Network for Automatic Segmentation and Classification of Coronary Artery Plaques in Cardiac CTA
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    Chapter 15 FaaStest - Machine Learning Based Cost and Performance FaaS Optimization
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    Chapter 16 Semi-automated Image Segmentation of the Midsystolic Left Ventricular Mitral Valve Complex in Ischemic Mitral Regurgitation
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    Chapter 16 Secure Query Processing over Encrypted Data Using a Distributed Index Structure for Outsourcing Sensitive Data
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    Chapter 17 An Empirical Study on Performance Server Analysis and URL Phishing Prevention to Improve System Management Through Machine Learning
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    Chapter 17 Atrial Scar Segmentation via Potential Learning in the Graph-Cut Framework
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    Chapter 18 Using Machine Learning for Identifying Ping Failure in Large Network Topology
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    Chapter 18 4D Cardiac Motion Modeling Using Pair-Wise Mesh Registration
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    Chapter 19 ISACHI: Integrated Segmentation and Alignment Correction for Heart Images
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    Chapter 19 Smart Contracts for Container Based Video Conferencing Services: Architecture and Implementation
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    Chapter 20 Deploying Blockchains for a New Paradigm of Media Experience
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    Chapter 20 3D LV Probabilistic Segmentation in Cardiac MRI Using Generative Adversarial Network
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    Chapter 21 Is Arbitrage Possible in the Bitcoin Market? (Work-In-Progress Paper)
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    Chapter 21 A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data
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    Chapter 22 Centreline-Based Shape Descriptors of the Left Atrial Appendage in Relation with Thrombus Formation
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    Chapter 23 Automatic 3D Atrial Segmentation from GE-MRIs Using Volumetric Fully Convolutional Networks
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    Chapter 24 Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss
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    Chapter 25 Fully Automated Left Atrium Cavity Segmentation from 3D GE-MRI by Multi-atlas Selection and Registration
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    Chapter 26 Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation
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    Chapter 27 Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation
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    Chapter 28 Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation
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    Chapter 29 Segmentation of the Left Atrium from 3D Gadolinium-Enhanced MR Images with Convolutional Neural Networks
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    Chapter 30 V-FCNN: Volumetric Fully Convolution Neural Network for Automatic Atrial Segmentation
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    Chapter 31 Ensemble of Convolutional Neural Networks for Heart Segmentation
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    Chapter 32 Multi-task Learning for Left Atrial Segmentation on GE-MRI
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    Chapter 33 Left Atrial Segmentation Combining Multi-atlas Whole Heart Labeling and Shape-Based Atlas Selection
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    Chapter 34 Deep Learning Based Method for Left Atrial Segmentation in GE-MRI
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    Chapter 35 Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
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    Chapter 36 A Semantic-Wise Convolutional Neural Network Approach for 3-D Left Atrium Segmentation from Late Gadolinium Enhanced Magnetic Resonance Imaging
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    Chapter 37 Left Atrial Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning
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    Chapter 38 Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images
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    Chapter 39 Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation
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    Chapter 40 Left-Ventricle Quantification Using Residual U-Net
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    Chapter 41 Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship Learning
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    Chapter 42 Convexity and Connectivity Principles Applied for Left Ventricle Segmentation and Quantification
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    Chapter 43 Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation
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    Chapter 44 Automated Full Quantification of Left Ventricle with Deep Neural Networks
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    Chapter 45 ESU-P-Net: Cascading Network for Full Quantification of Left Ventricle from Cine MRI
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    Chapter 46 Left Ventricle Full Quantification via Hierarchical Quantification Network
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    Chapter 47 Automatic Left Ventricle Quantification in Cardiac MRI via Hierarchical Refinement of High-Level Features by a Salient Perceptual Grouping Model
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    Chapter 48 Cardiac MRI Left Ventricle Segmentation and Quantification: A Framework Combining U-Net and Continuous Max-Flow
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    Chapter 49 Multi-estimator Full Left Ventricle Quantification Through Ensemble Learning
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    Chapter 50 Left Ventricle Quantification Through Spatio-Temporal CNNs
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    Chapter 51 Full Quantification of Left Ventricle Using Deep Multitask Network with Combination of 2D and 3D Convolution on 2D + t Cine MRI
Overall attention for this book and its chapters
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Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-12029-0
978-3-03-012028-3, 978-3-03-012029-0

Mihaela Pop, Maxime Sermesant, Jichao Zhao, Shuo Li, Kristin McLeod, Alistair Young, Kawal Rhode, Tommaso Mansi

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 26%
Student > Bachelor 4 13%
Student > Master 4 13%
Researcher 3 10%
Other 2 6%
Other 3 10%
Unknown 7 23%
Readers by discipline Count As %
Engineering 11 35%
Computer Science 4 13%
Agricultural and Biological Sciences 2 6%
Medicine and Dentistry 2 6%
Energy 1 3%
Other 1 3%
Unknown 10 32%