Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, ... Part VI

Preface
Organization
Contents – Part VI
Image Reconstruction
Two-Stage Self-supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images
	1 Introduction
	2 Method
		2.1 Interpolation Network
		2.2 The First-Stage SSL Based on Synthesized LR-HR Image Pairs
		2.3 The Second-Stage SSL with Cycle-Consistency Constraint
	3 Experiments
		3.1 Dataset
		3.2 Experimental Design
		3.3 Implementation Details
		3.4 Experimental Results
	4 Conclusion
	References
Over-and-Under Complete Convolutional RNN for MRI Reconstruction
	1 Introduction
	2 Methodology
	3 Experiments and Results
	4 Discussion and Conclusion
	References
TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation
	1 Introduction
	2 Methods
		2.1 Proposed Framework
		2.2 Training Objectives
	3 Experiments and Results
		3.1 Settings
		3.2 Results and Analyses
	4 Conclusion
	References
Synthesizing Multi-tracer PET Images for Alzheimer's Disease Patients Using a 3D Unified Anatomy-Aware Cyclic Adversarial Network
	1 Introduction
	2 Methods
		2.1 Evaluation with Human Data
	3 Results
	4 Conclusion
	References
Generalised Super Resolution for Quantitative MRI Using Self-supervised Mixture of Experts
	1 Introduction
	2 Method
		2.1 Data Description
		2.2 Backbone Network Architecture
		2.3 Self-supervised Mixture of Experts
	3 Experiments
		3.1 Implementation Details
		3.2 Results
	4 Discussion and Conclusion
	References
TransCT: Dual-Path Transformer for Low Dose Computed Tomography
	1 Introduction
	2 Method
		2.1 TransCT
		2.2 Loss Function
		2.3 Implementation
	3 Experiments
		3.1 Ablation Study
	4 Conclusion
	References
IREM: High-Resolution Magnetic Resonance Image Reconstruction via Implicit Neural Representation
	1 Introduction
	2 Method
		2.1 Image Spatial Normalization
		2.2 Model Optimization
		2.3 HR Image Reconstruction
	3 Experiments
		3.1 Data
		3.2 Implementation Details
		3.3 Results
	4 Conclusion
	References
DA-VSR: Domain Adaptable Volumetric Super-Resolution for Medical Images
	1 Introduction
	2 Domain Adaptable Volumetric Super-Resolution
		2.1 Network Structure
		2.2 Self-supervised Adaptation
	3 Experiments
		3.1 Implementation Details
		3.2 Dataset
		3.3 Ablation Study
		3.4 Quantitative Evaluation
	4 Conclusion
	References
Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation
	1 Introduction and Motivation
	2 Problem Formulation
	3 Proposed Method
		3.1 HQS-CG Algorithm
		3.2 Dual-Domain Reconstruction Pipelines
	4 Experimental Results
		4.1 Datasets and Experimental Settings
		4.2 Ablation Study
		4.3 Quantitative and Qualitative Results Comparison
	5 Conclusion
	References
Fast Magnetic Resonance Imaging on Regions of Interest: From Sensing to Reconstruction
	1 Introduction
	2 Methods
		2.1 Problem Statement
		2.2 Adaptive Sampler
		2.3 Deep Reconstructor
		2.4 Training Strategy
	3 Implementations
	4 Experiments
		4.1 Data
		4.2 Results
	5 Conclusions
	References
InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction
	1 Introduction
	2 Method
		2.1 Optimization Algorithm
		2.2 Overview of InDuDoNet
	3 Experimental Results
		3.1 Ablation Study
		3.2 Performance Evaluation
	4 Conclusion
	References
Depth Estimation for Colonoscopy Images with Self-supervised Learning from Videos
	1 Introduction
	2 Methodology
		2.1 Training Baseline Model with Synthetic Data
		2.2 Self-supervision with Colonoscopy Videos
	3 Experiments
		3.1 Dataset and Implementation Details
		3.2 Quantitative Evaluation
		3.3 Qualitative Evaluation on Real Data
	4 Conclusion
	References
Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy
	1 Introduction
	2 Background
		2.1 Fluorescence Microscopy and Hadamard Sensing
		2.2 Sensing and Reconstruction Optimization
	3 Proposed Method
		3.1 End-to-End Sensing and Reconstruction Scheme
		3.2 Loss Function
		3.3 Implementation
	4 Experiments
		4.1 Masks
		4.2 Reconstruction Methods
	5 Conclusion
	References
Multi-contrast MRI Super-Resolution via a Multi-stage Integration Network
	1 Introduction
	2 Methodology
		2.1 Overall Architecture
		2.2 Multi-stage Integration Module
	3 Experiments
	4 Conclusion
	References
Generator Versus Segmentor: Pseudo-healthy Synthesis
	1 Introduction
	2 Methods
		2.1 Basic GVS Flowchart
		2.2 Improved Residual Loss
		2.3 Training a Segmentor with Strong Generalization Ability
	3 Experiments
		3.1 Implementation Details
		3.2 Evaluation Metrics
		3.3 Comparisons with Other Methods
		3.4 Ablation Study
		3.5 Results on LiTS Dataset
	4 Conclusions
	References
Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting
	1 Introduction
	2 Methods
		2.1 Problem Formulation
		2.2 Proposed Framework
		2.3 Sliding-Window Stacking of Spirals
		2.4 Learned Density Compensation
		2.5 Tissue Mapping via Agglomerated Neighboring Features
	3 Experiments and Results
	4 Conclusion
	References
Estimation of High Framerate Digital Subtraction Angiography Sequences at Low Radiation Dose
	1 Introduction and Related Work
	2 Methods
		2.1 Phase Decomposition Using Independent Component Analysis
		2.2 Training and Optimization
		2.3 Network Details
		2.4 Final Composition
	3 Results
	4 Conclusion
	References
RLP-Net: A Recursive Light Propagation Network for 3-D Virtual Refocusing
	1 Introduction
	2 Proposed Method
		2.1 Recursive Light Propagation Network (RLP-Net)
		2.2 Training RLP-Net
	3 Experiments
		3.1 Fluorescence Microscopy Dataset
		3.2 Training Details
		3.3 Evaluation Results
	4 Conclusion
	References
Noise Mapping and Removal in Complex-Valued Multi-Channel MRI via Optimal Shrinkage of Singular Values
	1 Introduction
	2 Methods
		2.1 Redundancy in MR Data
		2.2 Optimal Shrinkage of Singular Value and Noise Estimation
		2.3 Noise Mapping and Removal
	3 Experiments
		3.1 Numerical Validation
		3.2 In-Vivo High-Resolution Diffusion MRI
		3.3 In-Vivo Human Lung MRI
	4 Conclusion
	References
Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction
	1 Introduction
	2 Methodology
		2.1 Online Self-supervised Learning for Context Consistency
		2.2 Online Adversarial Learning for Shape Constraint
		2.3 Differentiable Reconstruction Approximation
		2.4 Loss Function
	3 Experiments
	4 Conclusion
	References
Universal Undersampled MRI Reconstruction
	1 Introduction
	2 Methods
		2.1 The Overall Framework
		2.2 Anatomy-SPecific Instance Normalization (ASPIN)
		2.3 Model Distillation
		2.4 Network Training Pipeline
	3 Experimental Results
		3.1 Datasets and Network Configuration
		3.2 Algorithm Comparison
		3.3 Ablation Study
		3.4 Model Complexity
	4 Conclusion
	References
A Neural Framework for Multi-variable Lesion Quantification Through B-Mode Style Transfer
	1 Introduction
	2 Method
		2.1 BQI-Net Architecture
		2.2 Training Details
	3 Experiments
		3.1 Numerical Simulation
		3.2 Phantom, and Ex-Vivo Measurements
	4 Conclusion
	References
Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction
	1 Introduction
	2 Method
		2.1 Deep ADMM as Backbone
		2.2 Temporal Feature Fusion Block
		2.3 Sampling Pattern Optimization Block
	3 Experiments
		3.1 Data Acquisition and Preprocessing
		3.2 Implementation Details and Ablation Study
		3.3 Performance Comparison
	4 Conclusion
	References
Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
	1 Introduction
	2 Method
		2.1 Problem Formulation
		2.2 The Proposed DAN-Net
	3 Experiments
		3.1 Dataset
		3.2 Implementation Details
		3.3 Comparison with State-of-the-Art Methods
		3.4 Clinical Study
	4 Ablation Study
	5 Conclusion
	References
Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution
	1 Introduction
	2 Related Work
	3 Method
		3.1 Data Description
		3.2 Models
	4 Experiments
	5 User Study
	6 Discussion and Conclusion
	References
Adaptive Squeeze-and-Shrink Image Denoising for Improving Deep Detection of Cerebral Microbleeds
	1 Introduction
	2 Adaptive Squeeze-and-Shrink Denoising
		2.1 Overall Framework
		2.2 Implicit Near-Optimal Sparse Representation
		2.3 The Denoising Procedure
	3 The Deep Detection of Cerebral Microbleeds
	4 Gaussian White Noise Removal
	5 Experiments on Real-World Data
		5.1 Data
		5.2 CMB Detection
	6 Discussion and Conclusions
	References
3D Transformer-GAN for High-Quality PET Reconstruction
	1 Introduction
	2 Methodology
		2.1 Architecture
		2.2 Objective Functions
		2.3 Training Details
	3 Experiments and Results
	4 Conclusion
	References
Learnable Multi-scale Fourier Interpolation for Sparse View CT Image Reconstruction
	1 Introduction
	2 Main Body
	3 Experiments
	4 Conclusion
	References
U-DuDoNet: Unpaired Dual-Domain Network for CT Metal Artifact Reduction
	1 Introduction
	2 Additive Property for Metal Artifacts
	3 Methodology
		3.1 Network Architecture
		3.2 Dual-Domain Cyclic Learning
	4 Experiment
		4.1 Experimental Setup
		4.2 Comparison on Simulated and Real Data
		4.3 Ablation Study
	5 Conclusion
	References
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
	1 Introduction
	2 Method
		2.1 Task Transformer Network
		2.2 Task Transformer Module
	3 Experiments
	4 Conclusion
	References
Conditional GAN with an Attention-Based Generator and a 3D Discriminator for 3D Medical Image Generation
	1 Introduction
	2 Method
	3 Experimental Results
	4 Conclusion
	References
Multimodal MRI Acceleration via Deep Cascading Networks with Peer-Layer-Wise Dense Connections
	1 Introduction
	2 Problem Formulation
	3 Proposed Method
	4 Experiments
	5 Conclusion
	References
Rician Noise Estimation for 3D Magnetic Resonance Images Based on Benford's Law
	1 Introduction
	2 Methodology
	3 Experimental Setting
	4 Results
	5 Conclusions
	References
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization
	1 Introduction
	2 System Model and Related Work
		2.1 Deep Learning for MRI Reconstruction
	3 Deep J-Sense: Unrolled Alternating Optimization
	4 Experimental Results
		4.1 Performance on Matching Test-Time Conditions
		4.2 Robustness to Test-Time Varying Acceleration Factors
		4.3 Robustness to Train-Time Varying ACS Size
	5 Discussion and Conclusions
	References
Label-Free Physics-Informed Image Sequence Reconstruction with Disentangled Spatial-Temporal Modeling
	1 Introduction
	2 Methodology
		2.1 Physics-Informed Loss Function
		2.2 Spatial Modeling via GCNN
		2.3 Temporal Modeling via Neural ODEs
	3 Experiments
		3.1 Synthetic Experiments
		3.2 Clinical Data
	4 Conclusion
	References
High-Resolution Hierarchical Adversarial Learning for OCT Speckle Noise Reduction
	1 Introduction
	2 Method
		2.1 Proposed Model
		2.2 Dataset
		2.3 Evaluation Metrics
	3 Experiments and Results
		3.1 Implementation Details
		3.2 Ablation Study
		3.3 Comparison Study
	4 Discussion
	5 Conclusion
	References
Self-supervised Learning for MRI Reconstruction with a Parallel Network Training Framework
	1 Introduction
	2 Methods
		2.1 Mathematical Model of CS-MRI Reconstruction
		2.2 Brief Recap of ISTA-Net
		2.3 Proposed Self-supervised Learning Method
		2.4 Implementation Details
	3 Experiments and Results
	4 Conclusion
	References
Acceleration by Deep-Learnt Sharing of Superfluous Information in Multi-contrast MRI
	1 Introduction
	2 Method
	3 Results
	4 Discussion
	5 Conclusion
	References
Sequential Lung Nodule Synthesis Using Attribute-Guided Generative Adversarial Networks
	1 Introduction
	2 Proposed Method
		2.1 Model Architecture
		2.2 Loss Functions
	3 Experimental Results
		3.1 Dataset and Implementation
		3.2 Analysis of Lung Nodule Synthesis and Computation Costs
		3.3 Visual Turing Test
		3.4 Ablation Study
	4 Conclusion
	References
A Data-Driven Approach for High Frame Rate Synthetic Transmit Aperture Ultrasound Imaging
	1 Introduction
	2 Methods
		2.1 Theory Basis and Network Architecture
		2.2 Training Configurations
		2.3 Simulations and In-Vivo Experiments
		2.4 Metrics
	3 Results
	4 Conclusion
	References
Interpretable Deep Learning for Multimodal Super-Resolution of Medical Images
	1 Introduction
	2 Sparse Modelling for Image Reconstruction
	3 Deep Unfolding
	4 A Multimodal Convolutional Deep Unfolding Design for Medical Image Super-Resolution
	5 Experiments
	6 Conclusion
	References
MRI Super-Resolution Through Generative Degradation Learning
	1 Introduction
	2 Methods
		2.1 Theory
		2.2 GDN-Based SRR
		2.3 Materials
		2.4 Experimental Design
	3 Results
	4 Discussion
	References
Task-Oriented Low-Dose CT Image Denoising
	1 Introduction
	2 Method
		2.1 WGAN for LDCT Denoising
		2.2 Analysis of Task-Oriented Loss
		2.3 Training Strategy
	3 Experiments
		3.1 Datasets
		3.2 Segmentation Networks
		3.3 Implementation Details
		3.4 Enhancement on Task-Related Regions
		3.5 Boosting Downstream Task Performance
	4 Conclusion
	References
Revisiting Contour-Driven and Knowledge-Based Deformable Models: Application to 2D-3D Proximal Femur Reconstruction from X-ray Images
	1 Introduction and Related Work
	2 Method and Material
	3 Results and Discussion
	4 Conclusion
	References
Memory-Efficient Learning for High-Dimensional MRI Reconstruction
	1 Introduction
	2 Methods
		2.1 Memory-Efficient Learning
		2.2 Memory-Efficient Learning for MoDL
		2.3 Training and Evaluation of Memory-Efficient Learning
	3 Results
	4 Conclusions
	References
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation
	1 Introduction
	2 Method
		2.1 Global Stream in SA-GAN
		2.2 Segmentation Network in the Local Stream
		2.3 Organ Style Transfer with AdaON
	3 Experiments and Results
	4 Conclusion
	References
Clinical Applications - Cardiac
Distortion Energy for Deep Learning-Based Volumetric Finite Element Mesh Generation for Aortic Valves
	1 Introduction
	2 Methods
		2.1 Template Deformation-Based Mesh Generation
		2.2 Distortion Energy (Larap)
		2.3 Weighted Larap (Lwarap)
	3 Experiments and Results
		3.1 Data Acquisition and Preprocessing
		3.2 Implementation Details
		3.3 Spatial Accuracy and Volumetric Mesh Quality
		3.4 FE Stress Analysis During Valve Closure
		3.5 Limitations and Future Works
	4 Conclusion
	References
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
	1 Introduction
	2 Method
	3 Experimentation
	4 Conclusion
	References
EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis
	1 Introduction
	2 The EchoCP Dataset
		2.1 Data Characteristics
		2.2 PFO Diagnosis and Evaluation Protocol
	3 Experiments of Baseline Method
	4 Results and Analysis
	5 Conclusion
	References
Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries
	1 Introduction
	2 Method
		2.1 Semantic Feature Extraction for Local Cubic Volumes
		2.2 Transformer Structure for Global Sequence Analysis
	3 Experiment
		3.1 Dataset
		3.2 Experimental Results
	4 Conclusion
	References
Training Automatic View Planner for Cardiac MR Imaging via Self-supervision by Spatial Relationship Between Views
	1 Introduction
	2 Methods
	3 Experiments
	4 Conclusion
	References
Phase-Independent Latent Representation for Cardiac Shape Analysis
	1 Introduction
	2 Methodology
		2.1 Pre-processing Pipeline
		2.2 Graph Representation of the LA
		2.3 Design of Fusion and Classification Loss Function
	3 Synthetic Experiments
		3.1 Noisy Labels
	4 Application to LAA Graphs
	5 Conclusion
	References
Cardiac Transmembrane Potential Imaging with GCN Based Iterative Soft Threshold Network
	1 Introduction
	2 Methodology
		2.1 GISTA-Net Architecture
		2.2 Implementation of Graph Convolution Network
	3 Experiments
		3.1 Ectopic Pacing Experiment
		3.2 Myocardial Infarction Experiment
		3.3 Cardiac Activation Sequence Reconstruction
	4 Discussion
	5 Conclusion
	References
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-center LGE MRIs
	1 Introduction
	2 Methodology
		2.1 Image Segmentation Models
		2.2 Domain Generalization Models
	3 Materials
		3.1 Data Acquisition and Pre-processing
		3.2 Gold Standard and Evaluation
		3.3 Implementation
	4 Experiment
		4.1 Comparisons of Different Semantic Segmentation Networks
		4.2 Comparisons of Post- and Pre-ablation LGE MRI
		4.3 Comparisons of Different Generalization Models
	5 Conclusion
	References
TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline
	1 Introduction
	2 Methods
		2.1 Imaging Data and Manual Annotation
		2.2 Dual-Stage Residual Neural Network
		2.3 Evaluation and Statistical Analysis
	3 Experiments and Results
		3.1 Implementation
		3.2 Annotation Accuracy
	4 Discussion and Conclusion
	References
Clinical Applications - Vascular
Deep Open Snake Tracker for Vessel Tracing
	1 Introduction
	2 Methods
		2.1 Deep Open Curve Snake
		2.2 Curve Proposal from Centerline Segmentation
		2.3 Deep Snake Tracing
		2.4 Global Tree Construction
	3 Experimental Settings and Results
		3.1 Datasets
		3.2 Evaluation Metrics
		3.3 Evaluation on BRAVE
		3.4 Ablation Study
		3.5 Adaptability of DOST on Other Datasets
	4 Discussions and Conclusion
	References
MASC-Units:Training Oriented Filters for Segmenting Curvilinear Structures
	1 Introduction
	2 Related Works
	3 Methods
		3.1 Rotatable MAC Unit and Response Shaping
		3.2 Filter Re-use
		3.3 Initialization Strategy
		3.4 Multi-scale Processing with Pyramids
	4 Experiments
	5 Conclusion
	References
Vessel Width Estimation via Convolutional Regression
	1 Introduction
	2 Method
		2.1 Vessel Width Label Generation Method
		2.2 Vessel Width Estimation Network
	3 Dataset
		3.1 Retinal Vessel Dataset for Width Estimation
		3.2 Coronary Artery Dataset for Width Estimation
	4 Experiment
		4.1 Retinal Vessel Width Estimation
		4.2 Coronary Artery Width Estimation
	5 Conclusion
	References
Renal Cell Carcinoma Classification from Vascular Morphology
	1 Introduction
	2 Related Works
		2.1 Histopathological Images Dataset
		2.2 Histopathological Images Classification
		2.3 Hand-Crafted Features
	3 Dataset
		3.1 Dataset Building
		3.2 VRCC200
	4 Vascular Network Feature
		4.1 Hand-Crafted Features
		4.2 Deep Learning Feature
	5 Experiments
		5.1 Skeleton Features and Lattice Features Analysis
		5.2 Vascular-Based RCC Classification Benchmark
	6 Conclusion
	References
Author Index