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Biomedical Image Synthesis and Simulation

Methods and Applications

  • 1st Edition - June 18, 2022
  • Latest edition
  • Editors: Ninon Burgos, David Svoboda
  • Language: English

Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biome… Read more

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Description

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.

Key features

  • 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

Readership

Graduate students and researchers in medical imaging

Table of contents

1. Introduction to Medical and Biomedical Image Synthesis

2. Parametric model-based approaches

3. Monte Carlo Simulations for Medical and Biomedical Applications

4. Medical Image Synthesis using Segmentation and Registration

5. Dictionary Learning for Medical Image Synthesis

6. Convolutional Neural Networks for Image Synthesis

7. Generative Adversarial Networks for Medical Image Synthesis

8. Autoencoders and Variational Autoencoders in Medical Image Analysis

9. Optimization of the MR Imaging Pipeline Using Simulation

10. Synthesis for Image Analysis Across Modalities: Application to Registration and Segmentation

11. Medical Image Harmonization Through Synthesis

12. Medical Image Super-Resolution With Deep Networks

13. Medical Image Denoising

14. Data Augmentation for Medical Image Analysis

15. Unsupervised Abnormality Detection in Medical Images with Deep Generative Methods

16. Regularising Disentangled Representations with Anatomical Temporal Consistency

17. Image Imputation in Cardiac MRI and Quality Assessment

18. Image Synthesis for Low-count PET Acquisitions: Lower Dose, Shorter Time

19. PET/MRI attenuation correction

20. Image Synthesis for MRI-only Radiotherapy Treatment Planning

21. Review of Cell Image Synthesis for Image Processing

22. Generative Models for Synthesis of Colorectal Cancer Histology Images

23. Spatiotemporal Image Generation for Embryomics Applications

24. Biomolecule Trafficking and Network Tomography-based Simulations

25. Validation and Evaluation Metrics for Medical and Biomedical Image Synthesis

26. Uncertainty Quantification in Medical Image Synthesis

27. Future trends

Product details

  • Edition: 1
  • Latest edition
  • Published: June 18, 2022
  • Language: English

About the editors

NB

Ninon Burgos

Ninon Burgos is a CNRS researcher at the Paris Brain Institute, in the ARAMIS Lab, and a fellow of PR[AI]RIE, the Paris Artificial Intelligence Research Institute, France. She completed her PhD at University College London, UK, with a thesis on image synthesis for the attenuation correction and analysis of hybrid positron emission tomography/magnetic resonance imaging data. In 2019, she received the ERCIM Cor Baayen Young Researcher Award. Her research focuses on the processing and analysis of medical images, the use of images to guide the diagnosis of neurological diseases, and the application of these methods to the clinic.
Affiliations and expertise
CNRS Researcher, Brain and Spine Institute (ICM), ARAMIS Lab, Paris, France

DS

David Svoboda

David Svoboda is an associate professor at the Department of visual computing of the Faculty of Informatics, Masaryk University, Brno, Czech Republic. He completed his PhD in computer science with a thesis on segmentation of volumetric histopathological images. He spent a half-year research visit at Manchester Metropolitan University, Manchester, UK, in the signal processing group, where he focused on the problems on edge detection using the statistics-based filtering. Since 2006, he has been with the Centre for Biomedical Image Analysis at Masaryk University. His current research fields include the manipulation of huge image data and the generation of synthetic microscopy image data, both static and time-lapse sequences.
Affiliations and expertise
Associate Professor of informatics MU Brno, Czech Republic

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