Biomedical Image Synthesis and Simulation Methods and Applications
Biomedical Image Synthesis and Simulations: Methods and Applications presents the latest on basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. Sections introduce and describe the simulation and synthesis methods that were developed and suc...
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| Médium: | E-kniha |
| Jazyk: | English |
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Chantilly
Elsevier Science & Technology
2022
Academic Press |
| Vydanie: | 1 |
| Edícia: | The MICCAI Society Book Series |
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| ISBN: | 012824349X, 9780128243497 |
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| Abstract | Biomedical Image Synthesis and Simulations: Methods and Applications presents the latest on basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. Sections introduce and describe the simulation and synthesis methods that were developed and successfully used within the last twenty years and give examples of successful applications of these methods. As the book provides a survey of all the commonly established approaches and more recent deep learning methods, it is highly suitable for graduate students and researchers in medical and biomedical imaging. |
|---|---|
| AbstractList | Biomedical Image Synthesis and Simulations: Methods and Applications presents the latest on basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. Sections introduce and describe the simulation and synthesis methods that were developed and successfully used within the last twenty years and give examples of successful applications of these methods. As the book provides a survey of all the commonly established approaches and more recent deep learning methods, it is highly suitable for graduate students and researchers in medical and biomedical imaging. 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. |
| Author | Svoboda, David Burgos, Ninon |
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| PublicationDate | 2022 2022-06-30 |
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| Snippet | Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used... Biomedical Image Synthesis and Simulations: Methods and Applications presents the latest on basic concepts and applications in image-based simulation and... |
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| SubjectTerms | Biomedical engineering Diagnostic imaging Image analysis |
| Subtitle | Methods and Applications |
| TableOfContents | Front Cover -- Biomedical Image Synthesis and Simulation -- Copyright -- Contents -- Contributors -- Preface -- 1 Introduction to medical and biomedical image synthesis -- Part 1 Methods and principles -- 2 Parametric modeling in biomedical image synthesis -- 2.1 Introduction -- 2.2 Parametric modeling paradigm -- 2.2.1 Modeling of the cellular objects -- 2.2.1.1 Generic parameter-controlled shape modeling: random shape model for nucleus and cell body -- 2.2.1.2 Cell-type specific parametric shape models -- 2.2.1.3 Modeling appearance: texture and subcellular organelle models -- 2.2.1.4 Modeling spatial distribution and populations -- 2.2.2 Modeling microscopy and image acquisition: from object models to simulated microscope images -- 2.3 On learning the parameters -- 2.4 Use cases -- 2.4.1 SIMCEP: parametric modeling framework aimed for generating and understanding microscopy images of cells -- 2.4.2 Simulated data for benchmarking -- 2.5 Future directions -- 2.6 Summary -- Acknowledgments -- References -- 3 Monte Carlo simulations for medical and biomedical applications -- 3.1 Introduction -- 3.1.1 A brief history -- 3.1.2 Monte Carlo method and biomedical physics -- 3.2 Underlying theory and principles -- 3.3 Particle transport through matter -- 3.3.1 Photon physics effects -- 3.3.2 Cross-section and mean free path -- 3.3.3 Models -- 3.3.4 Particle transport -- 3.4 Monte Carlo simulation structure -- 3.4.1 Particle source model -- 3.4.1.1 Analytical source -- 3.4.1.2 Voxelized source -- 3.4.1.3 Cumulative density function -- 3.4.1.4 Time management -- 3.4.1.5 Phase space -- 3.4.2 Digitized phantom -- 3.4.2.1 Matter composition -- 3.4.2.2 Analytical geometry -- 3.4.2.3 Voxelized geometry -- 3.4.2.4 Tessellated geometry -- 3.4.2.5 Mixed geometry -- 3.4.2.6 Hierarchical geometry and space partitioning data structure -- 3.4.3 Particle detector 12.3.2.5 Super-resolution for cell quantification and motion tracking 3.5 Running a Monte Carlo simulation -- 3.6 Improving Monte Carlo simulation efficiency -- 3.6.1 Woodcock tracking -- 3.6.2 GPU -- 3.6.3 Fixed force detection -- 3.6.4 Angular response functions -- 3.7 Examples of Monte Carlo simulation applications in medical physics -- 3.8 Monte Carlo simulation for computational biology -- 3.8.1 Generalization of the Monte Carlo method -- 3.8.2 Examples of computational biology applications -- 3.9 Summary -- References -- 4 Medical image synthesis using segmentation and registration -- 4.1 Introduction -- 4.2 Segmentation-based image synthesis -- 4.2.1 Segmentation approaches -- 4.2.1.1 Manual segmentation -- 4.2.1.2 Automatic segmentation -- 4.2.2 Intensity assignment approaches -- 4.2.2.1 Segmentation methods with bulk assignment -- 4.2.2.2 Segmentation methods with subject-specific assignment -- 4.3 Registration-based image synthesis -- 4.3.1 Single-atlas registration approaches -- 4.3.1.1 Direct multimodal registration -- 4.3.1.2 Indirect unimodal registration -- 4.3.2 Multi-atlas registration approaches -- 4.3.3 Combination of registration and regression approaches -- 4.4 Hybrid approaches combining segmentation and registration -- 4.5 Future directions and research challenges -- 4.6 Summary -- Acknowledgments -- References -- 5 Dictionary learning for medical image synthesis -- 5.1 Introduction -- 5.2 Sparse coding -- 5.2.1 Orthogonal matching pursuit -- 5.3 Dictionary learning -- 5.4 Medical image synthesis with dictionary learning -- 5.5 Future directions and research challenges -- 5.6 Summary -- Acknowledgments -- References -- 6 Convolutional neural networks for image synthesis -- 6.1 Convolutional neural networks for image synthesis -- 6.2 Neural network building blocks -- 6.2.1 Neuron -- 6.2.2 Activation function -- 6.2.3 Generator layer details -- 6.3 Training a convolutional neural network 8.2.1.1 Sparse autoencoders -- 8.2.1.2 Contractive autoencoders -- 8.2.1.3 Denoising autoencoders -- 8.2.2 Summary -- 8.3 Variational autoencoders -- 8.3.1 The evidence lower bound (ELBO) -- 8.3.2 Implementation and optimization of variational autoencoders -- 8.3.3 Advantages and challenges of variational autoencoders -- 8.3.3.1 Current challenges of variational autoencoders -- 8.3.4 Disentanglement of the latent space -- 8.3.5 Alternative reconstruction objectives -- 8.3.6 Improving the flexibility of the model -- 8.3.6.1 Alternative priors and auxiliary variables -- 8.3.6.2 Importance weighted autoencoder -- 8.3.6.3 Adversarial autoencoders -- 8.4 Example applications -- 8.4.1 Unsupervised pathology detection -- 8.4.2 Image synthesis for the explanation of black-box classifiers -- 8.4.3 Decoupled shape and appearance modeling for multimodal data -- 8.5 Future directions and research challenges -- 8.6 Summary -- References -- Part 2 Applications -- 9 Optimization of the MR imaging pipeline using simulation -- 9.1 Overview -- 9.2 History of MRI simulation -- 9.2.1 Diffusion MRI -- 9.3 The POSSUM simulation framework -- 9.3.1 POSSUM for MRI and functional MRI -- 9.3.1.1 Modeling artifacts -- 9.3.2 POSSUM for diffusion MRI -- 9.4 Applications -- 9.4.1 Motion correction algorithms for fMRI -- 9.4.1.1 MCFLIRT algorithm -- 9.4.1.2 Simulations -- 9.4.1.3 Results -- 9.4.2 Motion and eddy-current correction algorithms for diffusion MRI -- 9.4.3 Investigating the susceptibility-by-movement artifact -- 9.4.4 Investigating and optimizing image acquisition -- 9.4.5 Simulated data for machine learning -- 9.5 Future directions and research challenges -- References -- 10 Synthesis for image analysis across modalities -- 10.1 General motivation -- 10.2 Registration -- 10.2.1 Background -- 10.2.2 Similarity metrics and their limitations 10.2.3 Synthesis-based similarity metrics -- 10.2.4 Other applications of synthesis-based registration -- 10.3 Segmentation -- 10.3.1 Background -- 10.3.2 Domain gap and synthesis-based solutions -- 10.4 Other directions and perspectives -- References -- 11 Medical image harmonization through synthesis -- 11.1 Introduction -- 11.2 Supervised techniques -- 11.2.1 Architecture and training -- 11.2.2 Using more information -- 11.3 Unsupervised techniques -- 11.3.1 Generative adversarial networks -- 11.3.2 Learning interpretable representations -- 11.3.3 One-/few-shot harmonization -- 11.3.4 Conclusion -- References -- 12 Medical image super-resolution with deep networks -- 12.1 Introduction to super-resolution -- 12.1.1 Basic concepts -- 12.1.2 Brief history of SR methods prior to deep networks -- 12.1.2.1 SR through mathematical modeling -- 12.1.2.2 Example-based SR -- 12.2 SR methods with deep networks -- 12.2.1 Data acquisition -- 12.2.1.1 Fully-supervised, unsupervised, and self-supervised learning -- 12.2.1.2 Multiple network inputs -- 12.2.2 Network architectures -- 12.2.2.1 General frameworks -- 12.2.2.2 Upsampling before or within networks -- 12.2.2.3 Components in networks -- 12.2.2.4 Progressive networks -- 12.2.3 Loss functions -- 12.2.3.1 Paired losses -- 12.2.3.2 Unpaired losses -- 12.3 Applications of super-resolution in medical images -- 12.3.1 Super-resolution in different image modalities -- 12.3.1.1 Super-resolution in CT -- 12.3.1.2 Super-resolution in MRI -- 12.3.1.3 Super-resolution in optical coherence tomography -- 12.3.1.4 Super-resolution in microscopy -- 12.3.2 Super-resolution used for different tasks -- 12.3.2.1 Super-resolution for image quality enhancement -- 12.3.2.2 Super-resolution for diagnostic acceptability -- 12.3.2.3 Super-resolution for segmentation -- 12.3.2.4 Super-resolution for clinical abnormality detection 6.3.1 Loss functions -- 6.3.2 Back propagation -- 6.3.3 Image synthesis accuracy -- 6.4 Practical aspects -- 6.4.1 Pooling layers -- 6.4.2 Convolutional versus fully connected neural networks -- 6.4.3 Vanishing gradient -- 6.5 Commonly known networks -- 6.5.1 AlexNet -- 6.5.2 UNet -- 6.5.3 Inception network -- 6.6 Conclusion -- References -- 7 Generative adversarial networks for medical image synthesis -- 7.1 Introduction -- 7.2 Generative adversarial networks -- 7.2.1 Network architecture -- 7.2.1.1 Deep convolutional GANs -- 7.2.2 Loss function -- 7.2.2.1 Discriminator loss -- 7.2.2.2 Adversarial loss -- 7.2.3 Challenges of training GANs -- 7.3 Conditional GANs -- 7.3.1 Network architecture -- 7.3.2 Loss function -- 7.3.2.1 Image distance loss -- 7.3.2.2 Histogram matching loss -- 7.3.2.3 Perceptual loss -- 7.3.3 Variants of cGANs -- 7.3.3.1 Pix2pix -- 7.3.3.2 InfoGAN -- 7.4 Cycle GAN -- 7.4.1 Network architecture -- 7.4.2 Loss function: cycle consistency loss -- 7.4.3 Variants of Cycle GAN -- 7.4.3.1 Residual Cycle-GAN -- 7.4.3.2 Dense Cycle-GAN -- 7.4.3.3 Unsupervised image-to-image translation networks (UNIT) -- 7.4.3.4 Bicycle-GAN -- 7.4.3.5 StarGAN -- 7.5 Practical aspects -- 7.5.1 Network input dimension and size -- 7.5.2 Pre-processing -- 7.5.3 Data augmentation -- 7.6 CGAN and Cycle-GAN applications -- 7.6.1 Multi-modal MRI synthesis -- 7.6.2 MRI-only radiation therapy treatment planning -- 7.6.3 Image quality improvement/enhancement -- 7.6.4 Cell synthesis -- 7.7 Summary and discussion -- Disclosures -- References -- 8 Autoencoders and variational autoencoders in medical image analysis -- 8.1 Introduction -- 8.1.1 History of the method -- 8.1.2 Autoencoders and variational autoencoders in biomedical image analysis and synthesis -- 8.1.3 Outline of this chapter and notation -- 8.2 Autoencoders -- 8.2.1 Regularized autoencoders |
| Title | Biomedical Image Synthesis and Simulation |
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