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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

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Table of Contents

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    Book Overview
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    Chapter 1 Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
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    Chapter 2 Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound
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    Chapter 3 Unsupervised Quality Control of Image Segmentation Based on Bayesian Learning
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    Chapter 4 One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation
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    Chapter 5 ‘Project & Excite’ Modules for Segmentation of Volumetric Medical Scans
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    Chapter 6 Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation
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    Chapter 7 Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
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    Chapter 8 Extreme Points Derived Confidence Map as a Cue for Class-Agnostic Interactive Segmentation Using Deep Neural Network
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    Chapter 9 Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
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    Chapter 10 Instance Segmentation from Volumetric Biomedical Images Without Voxel-Wise Labeling
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    Chapter 11 Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice
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    Chapter 12 Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
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    Chapter 13 HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images
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    Chapter 14 PHiSeg: Capturing Uncertainty in Medical Image Segmentation
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    Chapter 15 Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
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    Chapter 16 Supervised Uncertainty Quantification for Segmentation with Multiple Annotations
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    Chapter 17 3D Tiled Convolution for Effective Segmentation of Volumetric Medical Images
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    Chapter 18 Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation
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    Chapter 19 Statistical Intensity- and Shape-Modeling to Automate Cerebrovascular Segmentation from TOF-MRA Data
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    Chapter 20 Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences Using Contextual Memory
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    Chapter 21 Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT Using Two-Stream Chained 3D Deep Network Fusion
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    Chapter 22 Mixed-Supervised Dual-Network for Medical Image Segmentation
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    Chapter 23 Fully Automated Pancreas Segmentation with Two-Stage 3D Convolutional Neural Networks
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    Chapter 24 Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation
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    Chapter 25 Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images
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    Chapter 26 Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation
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    Chapter 27 Radiomics-guided GAN for Segmentation of Liver Tumor Without Contrast Agents
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    Chapter 28 Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks
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    Chapter 29 Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
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    Chapter 30 Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
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    Chapter 31 Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
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    Chapter 32 Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation
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    Chapter 33 3D U $$^2$$ -Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation
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    Chapter 34 Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
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    Chapter 35 Multi-resolution Path CNN with Deep Supervision for Intervertebral Disc Localization and Segmentation
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    Chapter 36 Automatic Paraspinal Muscle Segmentation in Patients with Lumbar Pathology Using Deep Convolutional Neural Network
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    Chapter 37 Constrained Domain Adaptation for Segmentation
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    Chapter 38 Image-and-Spatial Transformer Networks for Structure-Guided Image Registration
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    Chapter 39 Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration
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    Chapter 40 A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images
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    Chapter 41 TopAwaRe: Topology-Aware Registration
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    Chapter 42 Multimodal Data Registration for Brain Structural Association Networks
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    Chapter 43 Dual-Stream Pyramid Registration Network
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    Chapter 44 A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration
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    Chapter 45 Conditional Segmentation in Lieu of Image Registration
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    Chapter 46 On the Applicability of Registration Uncertainty
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    Chapter 47 DeepAtlas: Joint Semi-supervised Learning of Image Registration and Segmentation
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    Chapter 48 Linear Time Invariant Model Based Motion Correction (LiMo-MoCo) of Dynamic Radial Contrast Enhanced MRI
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    Chapter 49 Incompressible Image Registration Using Divergence-Conforming B-Splines
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    Chapter 50 Direct Quantification for Coronary Artery Stenosis Using Multiview Learning
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    Chapter 51 Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
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    Chapter 52 Discriminative Coronary Artery Tracking via 3D CNN in Cardiac CT Angiography
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    Chapter 53 Whole Heart and Great Vessel Segmentation in Congenital Heart Disease Using Deep Neural Networks and Graph Matching
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    Chapter 54 Harmonic Balance Techniques in Cardiovascular Fluid Mechanics
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    Chapter 55 Deep Learning Within a Priori Temporal Feature Spaces for Large-Scale Dynamic MR Image Reconstruction: Application to 5-D Cardiac MR Multitasking
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    Chapter 56 k - t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations
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    Chapter 57 Model-Based Reconstruction for Highly Accelerated First-Pass Perfusion Cardiac MRI
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    Chapter 58 Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images
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    Chapter 59 Right Ventricle Segmentation in Short-Axis MRI Using a Shape Constrained Dense Connected U-Net
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    Chapter 60 Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
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    Chapter 61 A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation
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    Chapter 62 Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
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    Chapter 63 Curriculum Semi-supervised Segmentation
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    Chapter 64 A Multi-modality Network for Cardiomyopathy Death Risk Prediction with CMR Images and Clinical Information
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    Chapter 65 3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
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    Chapter 66 Discriminative Consistent Domain Generation for Semi-supervised Learning
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    Chapter 67 Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
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    Chapter 68 MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation
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    Chapter 69 The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN
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    Chapter 70 Cardiac MRI Segmentation with Strong Anatomical Guarantees
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    Chapter 71 Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images
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    Chapter 72 Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets
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    Chapter 73 Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks
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    Chapter 74 Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation
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    Chapter 75 Recurrent Aggregation Learning for Multi-view Echocardiographic Sequences Segmentation
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    Chapter 76 Echocardiography View Classification Using Quality Transfer Star Generative Adversarial Networks
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    Chapter 77 Dual-View Joint Estimation of Left Ventricular Ejection Fraction with Uncertainty Modelling in Echocardiograms
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    Chapter 78 Frame Rate Up-Conversion in Echocardiography Using a Conditioned Variational Autoencoder and Generative Adversarial Model
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    Chapter 79 Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
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    Chapter 80 DeepAAA: Clinically Applicable and Generalizable Detection of Abdominal Aortic Aneurysm Using Deep Learning
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    Chapter 81 Texture-Based Classification of Significant Stenosis in CCTA Multi-view Images of Coronary Arteries
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    Chapter 82 Fourier Spectral Dynamic Data Assimilation: Interlacing CFD with 4D Flow MRI
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    Chapter 83 Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging
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    Chapter 84 HFA-Net: 3D Cardiovascular Image Segmentation with Asymmetrical Pooling and Content-Aware Fusion
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    Chapter 85 Spectral CT Based Training Dataset Generation and Augmentation for Conventional CT Vascular Segmentation
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    Chapter 86 Context-Aware Inductive Bias Learning for Vessel Border Detection in Multi-modal Intracoronary Imaging
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    Chapter 87 Neural Parameters Estimation for Brain Tumor Growth Modeling
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    Chapter 88 Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation
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    Chapter 89 Deep Probabilistic Modeling of Glioma Growth
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    Chapter 90 Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brain
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    Chapter 91 Variational AutoEncoder for Regression: Application to Brain Aging Analysis
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    Chapter 92 Early Development of Infant Brain Complex Network
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    Chapter 93 Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties
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    Chapter 94 Continually Modeling Alzheimer’s Disease Progression via Deep Multi-order Preserving Weight Consolidation
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    Chapter 95 Disease Knowledge Transfer Across Neurodegenerative Diseases
Overall attention for this book and its chapters
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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Published by
Springer International Publishing, December 2019
DOI 10.1007/978-3-030-32245-8
978-3-03-032244-1, 978-3-03-032245-8

Shen, Dinggang, Liu, Tianming, Peters, Terry M., Staib, Lawrence H., Essert, Caroline, Zhou, Sean, Yap, Pew-Thian, Khan, Ali

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 215 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 215 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 22%
Student > Master 34 16%
Researcher 17 8%
Student > Bachelor 14 7%
Student > Doctoral Student 8 4%
Other 17 8%
Unknown 77 36%
Readers by discipline Count As %
Computer Science 73 34%
Engineering 35 16%
Mathematics 5 2%
Biochemistry, Genetics and Molecular Biology 4 2%
Medicine and Dentistry 4 2%
Other 9 4%
Unknown 85 40%