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Image Analysis for Moving Organ, Breast, and Thoracic Images

Overview of attention for book
Cover of 'Image Analysis for Moving Organ, Breast, and Thoracic Images'

Table of Contents

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    Book Overview
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    Chapter 1 Resection-Based Demons Regularization for Breast Tumor Bed Propagation
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    Chapter 2 Linear and Deformable Image Registration with 3D Convolutional Neural Networks
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    Chapter 3 Super Resolution of Cardiac Cine MRI Sequences Using Deep Learning
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    Chapter 4 Automated CNN-Based Reconstruction of Short-Axis Cardiac MR Sequence from Real-Time Image Data
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    Chapter 5 An Unbiased Groupwise Registration Algorithm for Correcting Motion in Dynamic Contrast-Enhanced Magnetic Resonance Images
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    Chapter 6 Siamese Network for Dual-View Mammography Mass Matching
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    Chapter 7 Large-Scale Mammography CAD with Deformable Conv-Nets
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    Chapter 8 Domain Adaptation for Deviating Acquisition Protocols in CNN-Based Lesion Classification on Diffusion-Weighted MR Images
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    Chapter 9 Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-Net
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    Chapter 10 Improving Breast Cancer Detection Using Symmetry Information with Deep Learning
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    Chapter 11 Conditional Infilling GANs for Data Augmentation in Mammogram Classification
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    Chapter 12 A Unified Mammogram Analysis Method via Hybrid Deep Supervision
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    Chapter 13 Structure-Aware Staging for Breast Cancer Metastases
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    Chapter 14 Reproducible Evaluation of Registration Algorithms for Movement Correction in Dynamic Contrast Enhancing Magnetic Resonance Imaging for Breast Cancer Diagnosis
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    Chapter 15 Robust Windowed Harmonic Phase Analysis with a Single Acquisition
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    Chapter 16 Lung Structures Enhancement in Chest Radiographs via CT Based FCNN Training
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    Chapter 17 Improving the Segmentation of Anatomical Structures in Chest Radiographs Using U-Net with an ImageNet Pre-trained Encoder
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    Chapter 18 Tuberculosis Histopathology on X Ray CT
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    Chapter 19 A CT Scan Harmonization Technique to Detect Emphysema and Small Airway Diseases
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    Chapter 20 Transfer Learning for Segmentation of Injured Lungs Using Coarse-to-Fine Convolutional Neural Networks
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    Chapter 21 High Throughput Lung and Lobar Segmentation by 2D and 3D CNN on Chest CT with Diffuse Lung Disease
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    Chapter 22 Multi-structure Segmentation from Partially Labeled Datasets. Application to Body Composition Measurements on CT Scans
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    Chapter 23 3D Pulmonary Artery Segmentation from CTA Scans Using Deep Learning with Realistic Data Augmentation
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    Chapter 24 Automatic Airway Segmentation in Chest CT Using Convolutional Neural Networks
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    Chapter 25 Detecting Out-of-Phase Ventilation Using 4DCT to Improve Radiation Therapy for Lung Cancer
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    Chapter 26 XeMRI to CT Lung Image Registration Enhanced with Personalized 4DCT-Derived Motion Model
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    Chapter 27 Rigid Lens – Locally Rigid Approximations of Deformable Registration for Change Assessment in Thorax-Abdomen CT Follow-Up Scans
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    Chapter 28 Diffeomorphic Lung Registration Using Deep CNNs and Reinforced Learning
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    Chapter 29 Transfer Learning Approach to Predict Biopsy-Confirmed Malignancy of Lung Nodules from Imaging Data: A Pilot Study
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    Chapter 30 Convolutional Neural Network Based COPD and Emphysema Classifications Are Predictive of Lung Cancer Diagnosis
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    Chapter 31 Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography
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    Chapter 32 Automatic Classification of Centrilobular Emphysema on CT Using Deep Learning: Comparison with Visual Scoring
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    Chapter 33 On the Relevance of the Loss Function in the Agatston Score Regression from Non-ECG Gated CT Scans
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    Chapter 34 Accurate Measurement of Airway Morphology on Chest CT Images
Overall attention for this book and its chapters
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Mentioned by

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1 news outlet
blogs
1 blog
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15 X users
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8 patents

Citations

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19 Dimensions

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47 Mendeley
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Title
Image Analysis for Moving Organ, Breast, and Thoracic Images
Published by
Springer International Publishing, October 2018
DOI 10.1007/978-3-030-00946-5
ISBNs
978-3-03-000945-8, 978-3-03-000946-5
Editors

Stoyanov, Danail, Taylor, Zeike, Kainz, Bernhard, Maicas, Gabriel, Beichel, Reinhard R., Martel, Anne, Maier-Hein, Lena, Bhatia, Kanwal, Vercauteren, Tom, Oktay, Ozan, Carneiro, Gustavo, Bradley, Andrew P., Nascimento, Jacinto, Min, Hang, Brown, Matthew S., Jacobs, Colin, Lassen-Schmidt, Bianca, Mori, Kensaku, Petersen, Jens, San José Estépar, Raúl, Schmidt-Richberg, Alexander, Veiga, Catarina

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X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 23%
Student > Master 9 19%
Student > Bachelor 4 9%
Student > Postgraduate 3 6%
Student > Doctoral Student 2 4%
Other 4 9%
Unknown 14 30%
Readers by discipline Count As %
Engineering 15 32%
Computer Science 11 23%
Medicine and Dentistry 3 6%
Immunology and Microbiology 1 2%
Unspecified 1 2%
Other 2 4%
Unknown 14 30%

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