Simulation and Synthesis in Medical Imaging

Name: Simulation and Synthesis in Medical Imaging | 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings
Brand: Springer
Price: 58.84 EUR
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7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

Can Zhao David Svoboda Jelmer M. Wolterink Maria Escobar(Editor)

Springer (Publisher)

Published on 21. September 2022

VIII, 168 pages

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978-3-031-16980-9 (ISBN)

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Content

Intro
Preface
Organization
Contents
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
1 Introduction
1.1 Related Works
1.2 Contributions
2 Methods
2.1 Generators
2.2 Discriminators
2.3 Losses
3 Experiments
3.1 Evaluation
3.2 Implementation
3.3 Data
3.4 Results
4 Conclusion
References
Generating Artificial Artifacts for Motion Artifact Detection in Chest CT
1 Introduction
2 Methods
3 Experiments
4 Results
5 Discussion
References
Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data
1 Introduction
2 Probabilistic Image Diversification
3 Experiments and Results
3.1 Data Augmentation
3.2 Benchmarking
3.3 Test-Time Augmentation
4 Discussion and Conclusion
References
Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs
1 Introduction
1.1 Contributions
2 Method
2.1 Pathology Synthesis
2.2 Modeling Slice Relationship
2.3 Data and Implementation
3 Results
3.1 Pathology Synthesis
3.2 Modeling the Slice Relationship
4 Discussion and Conclusion
References
.26em plus .1em minus .1emHealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease
1 Introduction
2 HealthyGAN: The Proposed Method
2.1 Network Architecture
2.2 Training
2.3 Detecting Anomalies
3 Experiments and Results
3.1 COVID-19 Detection
3.2 Chest X-ray 14 Diseases Detection
3.3 Migraine Detection
4 Conclusion
A Implementation Details
B Network Architectures
B.1 Discriminator
B.2 Generator
References
Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement
1 Introduction
2 Methodology
3 Experiments and Results
4 Conclusion
References
Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain
1 Introduction
2 Background
2.1 VQ-VAE
2.2 Transformer
3 Methods
3.1 Descriptive Quantization for Transformer Usage
3.2 Autoregressive Modelling of the Brain
4 Experiments and Results
4.1 Quantitative Image Fidelity Evaluation
4.2 Morphological Evaluation
5 Conclusion
6 Appendix
6.1 VQ-VAEs
6.2 Transformers
6.3 Losses
6.4 Datasets
6.5 VBM Analysis
References
Can Segmentation Models Be Trained with Fully Synthetically Generated Data?
1 Background
2 Materials and Methods
2.1 Materials
2.2 Methods
2.3 Segmentation Network Used for the Experiments
3 Experiments
3.1 Can We Learn to Segment Healthy Regions Using Synthetic Data?
3.2 Can Synthetic Generative Models Address Out-of-Distribution Segmentation?
3.3 Can We Learn to Segment Pathologies from Synthetic Data?
4 Discussion and Conclusion
A Training Set-Ups
A.1 Training brainSPADE
A.2 Training Segmentation nnU-Nets
B Additional Figures
References
Multimodal Super Resolution with Dual Domain Loss and Gradient Guidance
1 Introduction
2 Materials and Methods
3 Experiments
4 Discussion and Conclusion
References
Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder
1 Introduction
2 Methodology
2.1 Model Architecture
2.2 Condition and Mask Embedding Blocks
2.3 Loss Functions
3 Experiments
3.1 Dataset
3.2 Implementation Details
3.3 Evaluation Metrics
3.4 Experimental Results
4 Discussion and Conclusion
References
Contrastive Learning for Generating Optical Coherence Tomography Images of the Retina
1 Introduction
2 Related Work
3 Methods
4 Experiments and Results
4.1 Dataset
4.2 Model Training
4.3 Results
5 Conclusions
References
A Novel Method Combining Global and Local Assessments to Evaluate CBCT-Based Synthetic CTs
1 Introduction
2 Methods
2.1 Data Acquisition and Processing
2.2 Validation Methodology
3 Results
3.1 Validation Methodology
4 Discussion
5 Conclusion
References
SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution
1 Introduction
2 Method
2.1 Feature Embedding
2.2 Volume Embedding
2.3 3D Deep Feature Extraction
2.4 HQ Volume Reconstruction
3 Experimental Setup
3.1 Implementation Details
3.2 Results
4 Conclusion
References
Evaluating the Performance of StyleGAN2-ADA on Medical Images
1 Introduction
2 Methods
2.1 Data
2.2 Generative Modeling
2.3 Evaluation Measures
3 Results
4 Conclusion
References
Backdoor Attack is a Devil in Federated GAN-Based Medical Image Synthesis
1 Introduction
2 Methods
2.1 Federated Generative Adversarial Network
2.2 Backdoor Attack Strategies
2.3 Defense Strategies
3 Experiments
3.1 Experimental Settings
3.2 Implementation of Attack
3.3 Implementation of Defense
3.4 Results and Discussion
4 Conclusion
A More Experiment Results
B WGAN-GP with Large Trigger Size
References
Author Index

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Simulation and Synthesis in Medical Imaging

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