Biomedical Image Synthesis and Simulation

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. 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.

• 1. Introduction to Medical and Biomedical Image Synthesis2. Parametric model-based approaches3. Monte Carlo Simulations for Medical and Biomedical Applications4. Medical Image Synthesis using Segmentation and Registration5. Dictionary Learning for Medical Image Synthesis6. Convolutional Neural Networks for Image Synthesis7. Generative Adversarial Networks for Medical Image Synthesis8. Autoencoders and Variational Autoencoders in Medical Image Analysis9. Optimization of the MR Imaging Pipeline Using Simulation10. Synthesis for Image Analysis Across Modalities: Application to Registration and Segmentation11. Medical Image Harmonization Through Synthesis12. Medical Image Super-Resolution With Deep Networks13. Medical Image Denoising14. Data Augmentation for Medical Image Analysis15. Unsupervised Abnormality Detection in Medical Images with Deep Generative Methods16. Regularising Disentangled Representations with Anatomical Temporal Consistency17. Image Imputation in Cardiac MRI and Quality Assessment18. Image Synthesis for Low-count PET Acquisitions: Lower Dose, Shorter Time19. PET/MRI attenuation correction20. Image Synthesis for MRI-only Radiotherapy Treatment Planning21. Review of Cell Image Synthesis for Image Processing22. Generative Models for Synthesis of Colorectal Cancer Histology Images23. Spatiotemporal Image Generation for Embryomics Applications24. Biomolecule Trafficking and Network Tomography-based Simulations25. Validation and Evaluation Metrics for Medical and Biomedical Image Synthesis26. Uncertainty Quantification in Medical Image Synthesis27. Future trends