Biomedical Image Synthesis and Simulation

1. Introduction to Medical and Biomedical Image Synthesis<br>2. Parametric model-based approaches<br>3. Monte Carlo Simulations for Medical and Biomedical Applications<br>4. Medical Image Synthesis using Segmentation and Registration<br>5. Dictionary Learning for Medical Image Synthesis<br>6. Convolutional Neural Networks for Image Synthesis<br>7. Generative Adversarial Networks for Medical Image Synthesis<br>8. Autoencoders and Variational Autoencoders in Medical Image Analysis<br>9. Optimization of the MR Imaging Pipeline Using Simulation<br>10. Synthesis for Image Analysis Across Modalities: Application to Registration and Segmentation<br>11. Medical Image Harmonization Through Synthesis<br>12. Medical Image Super-Resolution With Deep Networks<br>13. Medical Image Denoising<br>14. Data Augmentation for Medical Image Analysis<br>15. Unsupervised Abnormality Detection in Medical Images with Deep Generative Methods<br>16. Regularising Disentangled Representations with Anatomical Temporal Consistency<br>17. Image Imputation in Cardiac MRI and Quality Assessment<br>18. Image Synthesis for Low-count PET Acquisitions: Lower Dose, Shorter Time<br>19. PET/MRI attenuation correction<br>20. Image Synthesis for MRI-only Radiotherapy Treatment Planning<br>21. Review of Cell Image Synthesis for Image Processing<br>22. Generative Models for Synthesis of Colorectal Cancer Histology Images<br>23. Spatiotemporal Image Generation for Embryomics Applications<br>24. Biomolecule Trafficking and Network Tomography-based Simulations<br>25. Validation and Evaluation Metrics for Medical and Biomedical Image Synthesis<br>26. Uncertainty Quantification in Medical Image Synthesis<br>27. Future trends