Biomedical image synthesis and simulation : methods and applications /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Burgos, Ninon, Svoboda, David
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, UK : Academic Press, 2022.
Colección:MICCAI Society book series.
Temas:
Biomedical engineering.
Diagnostic imaging.
Image analysis.
Diagnostic Imaging
G�enie biom�edical.
Imagerie pour le diagnostic.
Analyse d'images.
biomedical engineering.
Biomedical engineering
Diagnostic imaging
Image analysis
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.
  • 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.

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