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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
Attention for Chapter 36: Automatic Paraspinal Muscle Segmentation in Patients with Lumbar Pathology Using Deep Convolutional Neural Network
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Chapter title
Automatic Paraspinal Muscle Segmentation in Patients with Lumbar Pathology Using Deep Convolutional Neural Network
Chapter number 36
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Published by
Springer, Cham, October 2019
DOI 10.1007/978-3-030-32245-8_36
Book ISBNs
978-3-03-032244-1, 978-3-03-032245-8
Authors

Wenyao Xia, Maryse Fortin, Joshua Ahn, Hassan Rivaz, Michele C. Battié, Terry M. Peters, Yiming Xiao

Twitter Demographics

The data shown below were collected from the profiles of 7 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 25%
Student > Bachelor 1 25%
Researcher 1 25%
Unknown 1 25%
Readers by discipline Count As %
Nursing and Health Professions 1 25%
Computer Science 1 25%
Unknown 2 50%