Biomedical Image Synthesis and Simulations

Biomedical Image Synthesis and Simulations

Methods and Applications

1st Edition - June 4, 2022

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  • Editors: Ninon Burgos, David Svoboda
  • Paperback ISBN: 9780128243497

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Description

Biomedical Image Synthesis and Simulations: Methods and Applications presents the latest on basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. Sections introduce and describe the simulation and synthesis methods that were developed and successfully used within the last twenty years and give examples of successful applications of these methods. As the book provides a survey of all the commonly established approaches and more recent deep learning methods, it is highly suitable for graduate students and researchers in medical and biomedical imaging.

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

  • Preface
    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

  • No. of pages: 668
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: June 4, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780128243497

About the Editors

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, on the use of images to guide the diagnosis of neurological diseases, and on the application of these methods to the clinic.

Affiliations and Expertise

CNRS Researcher, Brain and Spine Institute (ICM), ARAMIS Lab, Paris, France

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 histo-pathological 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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