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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
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    Chapter 27 Automatic Whole Heart Segmentation in CT Images Based on Multi-atlas Image Registration
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

twitter
26 tweeters
patent
7 patents
facebook
2 Facebook pages

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
17 Mendeley
Summary Twitter Patents Facebook Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges
Published by
arXiv, January 2017
DOI 10.1007/978-3-319-75541-0
ISBNs
978-3-31-975540-3, 978-3-31-975541-0
Authors

Fabian Isensee, Paul Jaeger, Peter M. Full, Ivo Wolf, Sandy Engelhardt, Klaus H. Maier-Hein

Editors

Mihaela Pop, Maxime Sermesant, Pierre-Marc Jodoin, Alain Lalande, Xiahai Zhuang, Guang Yang, Alistair Young, Olivier Bernard

View on publisher site

Twitter Demographics

The data shown below were collected from the profiles of 26 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 282%
Student > Master 20 118%
Researcher 14 82%
Student > Bachelor 13 76%
Professor > Associate Professor 5 29%
Other 18 106%
Readers by discipline Count As %
Computer Science 45 265%
Engineering 39 229%
Medicine and Dentistry 12 71%
Physics and Astronomy 2 12%
Business, Management and Accounting 2 12%
Other 6 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 14 February 2023.
All research outputs
#1,399,412
of 23,342,092 outputs
Outputs from arXiv
#21,490
of 961,798 outputs
Outputs of similar age
#31,016
of 423,016 outputs
Outputs of similar age from arXiv
#422
of 16,511 outputs
Altmetric has tracked 23,342,092 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 961,798 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 97% 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 423,016 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 16,511 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.

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