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(Herausgeber*in)

Springer (Verlag)

Erschienen am 21. September 2022

VIII, 168 Seiten

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

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Inhalt

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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