Biomedical Image Synthesis and Simulation
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Biomedical Image Synthesis and Simulation

Methods and Applications

1st Edition - June 18, 2022

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  • Editors: Ninon Burgos, David Svoboda
  • eBook ISBN: 9780128243503
  • Paperback ISBN: 9780128243497

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Description

Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future.

Key Features

  • Gives state-of-the-art methods in (bio)medical image synthesis
  • Explains the principles (background) of image synthesis methods
  • Presents the main applications of biomedical image synthesis methods

Readership

Graduate students and researchers in medical imaging

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Preface
  • Chapter 1: Introduction to medical and biomedical image synthesis
  • Abstract
  • Part 1: Methods and principles
  • Chapter 2: Parametric modeling in biomedical image synthesis
  • Abstract
  • Acknowledgements
  • 2.1. Introduction
  • 2.2. Parametric modeling paradigm
  • 2.3. On learning the parameters
  • 2.4. Use cases
  • 2.5. Future directions
  • 2.6. Summary
  • References
  • Chapter 3: Monte Carlo simulations for medical and biomedical applications
  • Abstract
  • 3.1. Introduction
  • 3.2. Underlying theory and principles
  • 3.3. Particle transport through matter
  • 3.4. Monte Carlo simulation structure
  • 3.5. Running a Monte Carlo simulation
  • 3.6. Improving Monte Carlo simulation efficiency
  • 3.7. Examples of Monte Carlo simulation applications in medical physics
  • 3.8. Monte Carlo simulation for computational biology
  • 3.9. Summary
  • References
  • Chapter 4: Medical image synthesis using segmentation and registration
  • Abstract
  • Acknowledgements
  • 4.1. Introduction
  • 4.2. Segmentation-based image synthesis
  • 4.3. Registration-based image synthesis
  • 4.4. Hybrid approaches combining segmentation and registration
  • 4.5. Future directions and research challenges
  • 4.6. Summary
  • References
  • Chapter 5: Dictionary learning for medical image synthesis
  • Abstract
  • Acknowledgements
  • 5.1. Introduction
  • 5.2. Sparse coding
  • 5.3. Dictionary learning
  • 5.4. Medical image synthesis with dictionary learning
  • 5.5. Future directions and research challenges
  • 5.6. Summary
  • References
  • Chapter 6: Convolutional neural networks for image synthesis
  • Abstract
  • 6.1. Convolutional neural networks for image synthesis
  • 6.2. Neural network building blocks
  • 6.3. Training a convolutional neural network
  • 6.4. Practical aspects
  • 6.5. Commonly known networks
  • 6.6. Conclusion
  • References
  • Chapter 7: Generative adversarial networks for medical image synthesis
  • Abstract
  • Disclosures
  • 7.1. Introduction
  • 7.2. Generative adversarial networks
  • 7.3. Conditional GANs
  • 7.4. Cycle GAN
  • 7.5. Practical aspects
  • 7.6. CGAN and Cycle-GAN applications
  • 7.7. Summary and discussion
  • References
  • Chapter 8: Autoencoders and variational autoencoders in medical image analysis
  • Abstract
  • 8.1. Introduction
  • 8.2. Autoencoders
  • 8.3. Variational autoencoders
  • 8.4. Example applications
  • 8.5. Future directions and research challenges
  • 8.6. Summary
  • References
  • Part 2: Applications
  • Chapter 9: Optimization of the MR imaging pipeline using simulation
  • Abstract
  • 9.1. Overview
  • 9.2. History of MRI simulation
  • 9.3. The POSSUM simulation framework
  • 9.4. Applications
  • 9.5. Future directions and research challenges
  • References
  • Chapter 10: Synthesis for image analysis across modalities
  • Abstract
  • 10.1. General motivation
  • 10.2. Registration
  • 10.3. Segmentation
  • 10.4. Other directions and perspectives
  • References
  • Chapter 11: Medical image harmonization through synthesis
  • Abstract
  • 11.1. Introduction
  • 11.2. Supervised techniques
  • 11.3. Unsupervised techniques
  • References
  • Chapter 12: Medical image super-resolution with deep networks
  • Abstract
  • 12.1. Introduction to super-resolution
  • 12.2. SR methods with deep networks
  • 12.3. Applications of super-resolution in medical images
  • 12.4. Conclusions
  • References
  • Chapter 13: Medical image denoising
  • Abstract
  • 13.1. Introduction
  • 13.2. Denoising approaches
  • 13.3. Evaluation metrics
  • 13.4. Examples of applications
  • 13.5. Summary
  • References
  • Chapter 14: Data augmentation for medical image analysis
  • Abstract
  • 14.1. Introduction
  • 14.2. Traditional methods for augmentation
  • 14.3. Synthesis-based methods
  • 14.4. Case study: data augmentation for retinal vessel segmentation
  • 14.5. Research challenges and future work
  • 14.6. Summary
  • References
  • Chapter 15: Unsupervised abnormality detection in medical images with deep generative methods
  • Abstract
  • 15.1. Overview
  • 15.2. Generative methods for unsupervised abnormality detection
  • 15.3. Application on real-world abnormalities
  • 15.4. Discussion
  • References
  • Chapter 16: Regularizing disentangled representations with anatomical temporal consistency
  • Abstract
  • Acknowledgements
  • 16.1. Introduction
  • 16.2. Related work
  • 16.3. Methods
  • 16.4. Experiments
  • 16.5. Results and discussion
  • 16.6. Conclusion
  • References
  • Chapter 17: Image imputation in cardiac MRI and quality assessment
  • Abstract
  • Acknowledgements
  • 17.1. Introduction
  • 17.2. Image imputation strategies
  • 17.3. Image imputation via conditional GAN
  • 17.4. Evaluation
  • 17.5. Research challenges and future directions
  • 17.6. Summary
  • References
  • Chapter 18: Image synthesis for low-count PET acquisitions: lower dose, shorter time
  • Abstract
  • 18.1. Introduction to low-count imaging
  • 18.2. Significance of low-count imaging
  • 18.3. Overview of methods and examples
  • 18.4. Future directions and research challenges
  • 18.5. Summary
  • References
  • Chapter 19: PET/MRI attenuation correction
  • Abstract
  • 19.1. Correction of photon attenuation
  • 19.2. Implications of inaccurate attenuation correction
  • 19.3. History of PET/MRI attenuation correction
  • 19.4. Comparison of attenuation correction methods
  • 19.5. State-of-the-art attenuation correction methods
  • 19.6. Future directions and remaining challenges
  • 19.7. Summary
  • References
  • Chapter 20: Image synthesis for MRI-only radiotherapy treatment planning
  • Abstract
  • 20.1. Introduction
  • 20.2. External beam radiation therapy summary
  • 20.3. Planning CT image acquisition
  • 20.4. Planning MRI acquisition
  • 20.5. Methods used for sCT generation
  • 20.6. Validation (with matching CT)
  • 20.7. Quality control (without matching CT)
  • 20.8. Deployment
  • 20.9. Summary
  • References
  • Chapter 21: Review of cell image synthesis for image processing
  • Abstract
  • Acknowledgements
  • 21.1. Introduction
  • 21.2. History
  • 21.3. Contemporary applications
  • 21.4. Summary
  • 21.5. Future work
  • References
  • Chapter 22: Generative models for synthesis of colorectal cancer histology images
  • Abstract
  • 22.1. Introduction
  • 22.2. Literature review
  • 22.3. Colorectal cancer tissue structure
  • 22.4. Model of spatial tumor heterogeneity
  • 22.5. Deep learning based colorectal pathology image generation
  • 22.6. Comparison
  • 22.7. Research challenges & future directions
  • References
  • Chapter 23: Spatiotemporal image generation for embryomics applications
  • Abstract
  • Acknowledgements
  • 23.1. Introduction
  • 23.2. Spatiotemporal simulation of virtual agents with realistic movement behavior
  • 23.3. Example applications
  • 23.4. Future directions and research challenges
  • 23.5. Summary
  • References
  • Further reading
  • Chapter 24: Biomolecule trafficking and network tomography-based simulations
  • Abstract
  • Acknowledgement
  • 24.1. Motivation
  • 24.2. Simulation for biomolecule trafficking analysis
  • 24.3. Applications
  • 24.4. Conclusion, future directions, and new challenges
  • 24.5. Summary
  • References
  • Further reading
  • Part 3: Perspectives
  • Chapter 25: Validation and evaluation metrics for medical and biomedical image synthesis
  • Abstract
  • Acknowledgement
  • 25.1. Introduction
  • 25.2. Expert knowledge
  • 25.3. Pairwise comparison
  • 25.4. Dataset comparison
  • 25.5. Conclusion
  • References
  • Chapter 26: Uncertainty quantification in medical image synthesis
  • Abstract
  • 26.1. Introduction
  • 26.2. Troublesome uncertainty landscape
  • 26.3. Tools for modeling uncertainty
  • 26.4. Open challenges
  • 26.5. Concluding remarks
  • References
  • Chapter 27: Future trends in medical and biomedical image synthesis
  • Abstract
  • Index

Product details

  • No. of pages: 674
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: June 18, 2022
  • Imprint: Academic Press
  • eBook ISBN: 9780128243503
  • Paperback ISBN: 9780128243497

About the Editors

Ninon Burgos

Ninon Burgos is a CNRS researcher at the Paris Brain Institute, in the ARAMIS Lab, and a fellow of PR[AI]RIE, the Paris Artificial Intelligence Research Institute, France. She completed her PhD at University College London, UK, with a thesis on image synthesis for the attenuation correction and analysis of hybrid positron emission tomography/magnetic resonance imaging data. In 2019, she received the ERCIM Cor Baayen Young Researcher Award. Her research focuses on the processing and analysis of medical images, the use of images to guide the diagnosis of neurological diseases, and the application of these methods to the clinic.

Affiliations and Expertise

CNRS Researcher, Brain and Spine Institute (ICM), ARAMIS Lab, Paris, France

David Svoboda

David Svoboda is an associate professor at the Department of visual computing of the Faculty of Informatics, Masaryk University, Brno, Czech Republic. He completed his PhD in computer science with a thesis on segmentation of volumetric histopathological images. He spent a half-year research visit at Manchester Metropolitan University, Manchester, UK, in the signal processing group, where he focused on the problems on edge detection using the statistics-based filtering. Since 2006, he has been with the Centre for Biomedical Image Analysis at Masaryk University. His current research fields include the manipulation of huge image data and the generation of synthetic microscopy image data, both static and time-lapse sequences.

Affiliations and Expertise

Associate Professor of informatics MU Brno, Czech Republic

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