rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Biomedical image synthesis and simulation
    sub title: methods and applications
  name:
    name part: Burgos, Ninon.
  name:
    name part: Svoboda, David.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    place:
      place term: London, UK
    publisher: Academic Press
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xvii, 656 pages) : illustrations
  abstract: 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.
  table of 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  note: edited by Ninon Burgos and David Svoboda.
  subject:
    topic: Biomedical engineering
  subject:
    topic: Diagnostic imaging
  subject:
    topic: Image analysis
  subject:
    topic: Diagnostic Imaging
  subject:
    topic: Génie biomédical
  subject:
    topic: Imagerie pour le diagnostic
  subject:
    topic: Analyse d'images
  subject:
    topic: biomedical engineering
  subject:
    topic: Biomedical engineering
  subject:
    topic: Diagnostic imaging
  subject:
    topic: Image analysis
  classification: R856
  classification: 610.28
  related item:
    identifier: (OCoLC)1260193401
  related item:
    title info:
      title: BIOMEDICAL IMAGE SYNTHESIS AND SIMULATIONS
    origin info:
      publisher: [S.l.] : ELSEVIER ACADEMIC PRESS, 2022
    identifier: (OCoLC)1260193401
  related item:
    title info:
      title: MICCAI Society book series
  identifier: 9780128243503
  identifier: 0128243503
  identifier: 9780128243497
  identifier: 012824349X
  identifier: 9780128243503 Ingram Content Group
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 220624
    record change date: 20260327172524.8
    record identifier: in00005769826
    language of cataloging:
      language term: eng