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Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges

Overview of attention for book
Cover of 'Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Multiview Machine Learning Using an Atlas of Cardiac Cycle Motion
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    Chapter 2 Joint Myocardial Registration and Segmentation of Cardiac BOLD MRI
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    Chapter 3 Transfer Learning for the Fully Automatic Segmentation of Left Ventricle Myocardium in Porcine Cardiac Cine MR Images
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    Chapter 4 Left Atrial Appendage Neck Modeling for Closure Surgery
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    Chapter 5 Detection of Substances in the Left Atrial Appendage by Spatiotemporal Motion Analysis Based on 4D-CT
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    Chapter 6 Estimation of Healthy and Fibrotic Tissue Distributions in DE-CMR Incorporating CINE-CMR in an EM Algorithm
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    Chapter 7 Multilevel Non-parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts
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    Chapter 8 GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation
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    Chapter 9 A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI
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    Chapter 10 Fast Fully-Automatic Cardiac Segmentation in MRI Using MRF Model Optimization, Substructures Tracking and B-Spline Smoothing
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    Chapter 11 Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
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    Chapter 12 An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
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    Chapter 13 Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features
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    Chapter 14 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation
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    Chapter 15 Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest
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    Chapter 16 Class-Balanced Deep Neural Network for Automatic Ventricular Structure Segmentation
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    Chapter 17 Automatic Segmentation of LV and RV in Cardiac MRI
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    Chapter 18 Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net
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    Chapter 19 3D Convolutional Networks for Fully Automatic Fine-Grained Whole Heart Partition
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    Chapter 20 Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations
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    Chapter 21 Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT
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    Chapter 22 Local Probabilistic Atlases and a Posteriori Correction for the Segmentation of Heart Images
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    Chapter 23 Hybrid Loss Guided Convolutional Networks for Whole Heart Parsing
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    Chapter 24 3D Deeply-Supervised U-Net Based Whole Heart Segmentation
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    Chapter 25 MRI Whole Heart Segmentation Using Discrete Nonlinear Registration and Fast Non-local Fusion
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    Chapter 26 Automatic Whole Heart Segmentation Using Deep Learning and Shape Context
  28. Altmetric Badge
    Chapter 27 Automatic Whole Heart Segmentation in CT Images Based on Multi-atlas Image Registration
Attention for Chapter 9: A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI
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About this Attention Score

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  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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9 X users

Citations

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28 Dimensions

Readers on

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53 Mendeley
Summary X Dimensions citations
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Chapter title
A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI
Chapter number 9
Book title
Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges
Published in
arXiv, September 2017
DOI 10.1007/978-3-319-75541-0_9
Book ISBNs
978-3-31-975540-3, 978-3-31-975541-0
Authors

Irem Cetin, Gerard Sanroma, Steffen E. Petersen, Sandy Napel, Oscar Camara, Miguel-Angel Gonzalez Ballester, Karim Lekadir

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 15%
Student > Master 6 11%
Researcher 5 9%
Lecturer 5 9%
Student > Bachelor 4 8%
Other 10 19%
Unknown 15 28%
Readers by discipline Count As %
Computer Science 17 32%
Engineering 7 13%
Medicine and Dentistry 4 8%
Agricultural and Biological Sciences 1 2%
Chemical Engineering 1 2%
Other 2 4%
Unknown 21 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 October 2019.
All research outputs
#14,362,528
of 24,998,746 outputs
Outputs from arXiv
#204,207
of 1,020,408 outputs
Outputs of similar age
#156,631
of 321,304 outputs
Outputs of similar age from arXiv
#3,813
of 17,921 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,020,408 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 78% 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 321,304 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 50% of its contemporaries.
We're also able to compare this research output to 17,921 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.

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