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.
1. Chapter 1 Introduction to medical and biomedical image synthesis - p. 1 2. Chapter 2 Parametric modeling in biomedical image synthesis - p. 7 3. Chapter 3 Monte Carlo simulations for medical and biomedical applications - p. 23 4. Chapter 4 Medical image synthesis using segmentation and registration - p. 55 5. Chapter 5 Dictionary learning for medical image synthesis - p. 79 6. Chapter 6 Convolutional neural networks for image synthesis - p. 91 7. Chapter 7 Generative adversarial networks for medical image synthesis - p. 105 8. Chapter 8 Autoencoders and variational autoencoders in medical image analysis - p. 129 9. Chapter 9 Optimization of the MR imaging pipeline using simulation - p. 165 10. Chapter 10 Synthesis for image analysis across modalities - p. 195 11. Chapter 11 Medical image harmonization through synthesis - p. 217 12. Chapter 12 Medical image super-resolution with deep networks - p. 233 13. Chapter 13 Medical image denoising - p. 255 14. Chapter 14 Data augmentation for medical image analysis - p. 279 15. Chapter 15 Unsupervised abnormality detection in medical images with deep generative methods - p. 303 16. Chapter 16 Regularizing disentangled representations with anatomical temporal consistency - p. 325 17. Chapter 17 Image imputation in cardiac MRI and quality assessment - p. 347 18. Chapter 18 Image synthesis for low-count PET acquisitions: lower dose, shorter time - p. 369 19. Chapter 19 PET/MRI attenuation correction - p. 393 20. Chapter 20 Image synthesis for MRI-only radiotherapy treatment planning - p. 423 21. Chapter 21 Review of cell image synthesis for image processing - p. 447 22. Chapter 22 Generative models for synthesis of colorectal cancer histology images - p. 491 23. Chapter 23 Spatiotemporal image generation for embryomics applications - p. 517 24. Chapter 24 Biomolecule trafficking and network tomography-based simulations - p. 543 25. Chapter 25 Validation and evaluation metrics for medical and biomedical image synthesis - p. 573 26. Chapter 26 Uncertainty quantification in medical image synthesis - p. 601 27. Chapter 27 Future trends in medical and biomedical image synthesis - p. 643 28. Index - p. 647
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.
1. Chapter 1 Introduction to medical and biomedical image synthesis - p. 1 2. Chapter 2 Parametric modeling in biomedical image synthesis - p. 7 3. Chapter 3 Monte Carlo simulations for medical and biomedical applications - p. 23 4. Chapter 4 Medical image synthesis using segmentation and registration - p. 55 5. Chapter 5 Dictionary learning for medical image synthesis - p. 79 6. Chapter 6 Convolutional neural networks for image synthesis - p. 91 7. Chapter 7 Generative adversarial networks for medical image synthesis - p. 105 8. Chapter 8 Autoencoders and variational autoencoders in medical image analysis - p. 129 9. Chapter 9 Optimization of the MR imaging pipeline using simulation - p. 165 10. Chapter 10 Synthesis for image analysis across modalities - p. 195 11. Chapter 11 Medical image harmonization through synthesis - p. 217 12. Chapter 12 Medical image super-resolution with deep networks - p. 233 13. Chapter 13 Medical image denoising - p. 255 14. Chapter 14 Data augmentation for medical image analysis - p. 279 15. Chapter 15 Unsupervised abnormality detection in medical images with deep generative methods - p. 303 16. Chapter 16 Regularizing disentangled representations with anatomical temporal consistency - p. 325 17. Chapter 17 Image imputation in cardiac MRI and quality assessment - p. 347 18. Chapter 18 Image synthesis for low-count PET acquisitions: lower dose, shorter time - p. 369 19. Chapter 19 PET/MRI attenuation correction - p. 393 20. Chapter 20 Image synthesis for MRI-only radiotherapy treatment planning - p. 423 21. Chapter 21 Review of cell image synthesis for image processing - p. 447 22. Chapter 22 Generative models for synthesis of colorectal cancer histology images - p. 491 23. Chapter 23 Spatiotemporal image generation for embryomics applications - p. 517 24. Chapter 24 Biomolecule trafficking and network tomography-based simulations - p. 543 25. Chapter 25 Validation and evaluation metrics for medical and biomedical image synthesis - p. 573 26. Chapter 26 Uncertainty quantification in medical image synthesis - p. 601 27. Chapter 27 Future trends in medical and biomedical image synthesis - p. 643 28. Index - p. 647
