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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
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

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Title
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Published by
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-319-28712-6
ISBNs
978-3-31-928711-9, 978-3-31-928712-6
Authors

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

Editors

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

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 100%
Readers by discipline Count As %
Engineering 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 April 2018.
All research outputs
#6,905,639
of 22,840,638 outputs
Outputs from Lecture notes in computer science
#2,253
of 8,127 outputs
Outputs of similar age
#111,131
of 393,572 outputs
Outputs of similar age from Lecture notes in computer science
#248
of 581 outputs
Altmetric has tracked 22,840,638 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 8,127 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,572 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 581 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.