Biomedical Image Synthesis and Simulation

Methods and Applications

Ninon Burgos, David Svoboda

• Publisher: Academic Press
• Year: 2022
• eBook ISBN: 9780128243503
• Edition: 0
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

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. - Gives state-of-the-art methods in (bio)medical image synthesis - Explains the principles (background) of image synthesis methods - Presents the main applications of biomedical image synthesis methods

Table of contents

• Biomedical Image Synthesis and Simulation
• Cover
• Title Page
• Copyright
• Contents
• Contributors
• Preface
• Chapter 1 Introduction to medical and biomedical image synthesis
• Chapter 2 Parametric modeling in biomedical image synthesis
• Chapter 3 Monte Carlo simulations for medical and biomedical applications
• Chapter 4 Medical image synthesis using segmentation and registration
• Chapter 5 Dictionary learning for medical image synthesis
• Chapter 6 Convolutional neural networks for image synthesis
• Chapter 7 Generative adversarial networks for medical image synthesis
• Chapter 8 Autoencoders and variational autoencoders in medical image analysis
• Chapter 9 Optimization of the MR imaging pipeline using simulation
• Chapter 10 Synthesis for image analysis across modalities
• Chapter 11 Medical image harmonization through synthesis
• Chapter 12 Medical image super-resolution with deep networks
• Chapter 13 Medical image denoising
• Chapter 14 Data augmentation for medical image analysis
• Chapter 15 Unsupervised abnormality detection in medical images with deep generative methods
• Chapter 16 Regularizing disentangled representations with anatomical temporal consistency
• Chapter 17 Image imputation in cardiac MRI and quality assessment
• Chapter 18 Image synthesis for low-count PET acquisitions: lower dose, shorter time
• Chapter 19 PET/MRI attenuation correction
• Chapter 20 Image synthesis for MRI-only radiotherapy treatment planning
• Chapter 21 Review of cell image synthesis for image processing
• Chapter 22 Generative models for synthesis of colorectal cancer histology images
• Chapter 23 Spatiotemporal image generation for embryomics applications
• Chapter 24 Biomolecule trafficking and network tomography-based simulations
• Chapter 25 Validation and evaluation metrics for medical and biomedical image synthesis
• Chapter 26 Uncertainty quantification in medical image synthesis
• Chapter 27 Future trends in medical and biomedical image synthesis
• Index