| Description |
1 online resource. |
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text rdacontent |
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computer rdamedia |
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online resource rdacarrier |
| Series |
The MICCAI Society book series |
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MICCAI Society book series.
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| Note |
Print version record. |
| Contents |
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. |
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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. |
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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. |
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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. |
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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. |
| Subject |
Biomedical engineering.
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Diagnostic imaging.
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Image analysis.
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Diagnostic Imaging |
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Génie biomédical.
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Imagerie pour le diagnostic.
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Analyse d'images.
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biomedical engineering.
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Biomedical engineering
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Diagnostic imaging
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Image analysis
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| Added Author |
Burgos, Ninon.
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Svoboda, David.
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| Other Form: |
Print version: 012824349X 9780128243497 (OCoLC)1260193401 |
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Print version: BIOMEDICAL IMAGE SYNTHESIS AND SIMULATIONS. [S.l.] : ELSEVIER ACADEMIC PRESS, 2022 012824349X (OCoLC)1260193401 |
| ISBN |
9780128243503 (electronic bk.) |
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0128243503 (electronic bk.) |
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9780128243497 |
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012824349X |
| Standard No. |
AU@ 000072198271 |
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UKMGB 020681217 |
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