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Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges

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Cover of 'Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges'

Table of Contents

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    Book Overview
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    Chapter 1 Automated Model-Based Left Ventricle Segmentation in Cardiac MR Images
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    Chapter 2 Beyond the AHA 17-Segment Model: Motion-Driven Parcellation of the Left Ventricle
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    Chapter 3 A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?
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    Chapter 4 Towards Left Ventricular Scar Localisation Using Local Motion Descriptors
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    Chapter 5 Traversed Graph Representation for Sparse Encoding of Macro-Reentrant Tachycardia
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    Chapter 6 Prediction of Infarct Localization from Myocardial Deformation
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    Chapter 7 Parameterisation of Multi-directional Diffusion Weighted Magnetic Resonance Images of the Heart
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    Chapter 8 Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging
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    Chapter 9 Papillary Muscle Segmentation from a Multi-atlas Database: A Feasibility Study
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    Chapter 10 Electrophysiology Model for a Human Heart with Ischemic Scar and Realistic Purkinje Network
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    Chapter 11 Patient Metadata-Constrained Shape Models for Cardiac Image Segmentation
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    Chapter 12 Myocardial Infarct Localization Using Neighbourhood Approximation Forests
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    Chapter 13 Systo-Diastolic LV Shape Analysis by Geometric Morphometrics and Parallel Transport Highly Discriminates Myocardial Infarction
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    Chapter 14 Statistical Shape Modeling Using Partial Least Squares: Application to the Assessment of Myocardial Infarction
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    Chapter 15 Classification of Myocardial Infarcted Patients by Combining Shape and Motion Features
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    Chapter 16 Detecting Myocardial Infarction Using Medial Surfaces
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    Chapter 17 Left Ventricle Classification Using Active Shape Model and Support Vector Machine
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    Chapter 18 Supervised Learning of Functional Maps for Infarct Classification
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    Chapter 19 Joint Clustering and Component Analysis of Spatio-Temporal Shape Patterns in Myocardial Infarction
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    Chapter 20 Myocardial Infarction Detection from Left Ventricular Shapes Using a Random Forest
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    Chapter 21 Combination of Polyaffine Transformations and Supervised Learning for the Automatic Diagnosis of LV Infarct
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    Chapter 22 Automatic Detection of Cardiac Remodeling Using Global and Local Clinical Measures and Random Forest Classification
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    Chapter 23 Automatic Detection of Myocardial Infarction Through a Global Shape Feature Based on Local Statistical Modeling
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    Chapter 24 Erratum to: Myocardial Infarction Detection from Left Ventricular Shapes Using a Random Forest
Attention for Chapter 20: Myocardial Infarction Detection from Left Ventricular Shapes Using a Random Forest
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Chapter title
Myocardial Infarction Detection from Left Ventricular Shapes Using a Random Forest
Chapter number 20
Book title
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Published in
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-319-28712-6_20
Book ISBNs
978-3-31-928711-9, 978-3-31-928712-6
Authors

Jack Allen, Ernesto Zacur, Erica Dall’Armellina, Pablo Lamata, Vicente Grau

Editors

Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Professor 2 22%
Researcher 2 22%
Other 1 11%
Unknown 1 11%
Readers by discipline Count As %
Computer Science 3 33%
Engineering 3 33%
Agricultural and Biological Sciences 2 22%
Unknown 1 11%