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

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

Table of Contents

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    Book Overview
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    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
Attention for 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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Citations

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Chapter title
Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest
Chapter number 15
Book title
Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges
Published by
Springer, Cham, September 2017
DOI 10.1007/978-3-319-75541-0_15
Book ISBNs
978-3-31-975540-3, 978-3-31-975541-0
Authors

Mahendra Khened, Varghese Alex, Ganapathy Krishnamurthi

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 21%
Student > Doctoral Student 3 7%
Student > Bachelor 3 7%
Lecturer 3 7%
Student > Master 3 7%
Other 6 14%
Unknown 16 37%
Readers by discipline Count As %
Computer Science 11 26%
Engineering 10 23%
Nursing and Health Professions 1 2%
Medicine and Dentistry 1 2%
Physics and Astronomy 1 2%
Other 0 0%
Unknown 19 44%

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