SASHIMI 2017: A MICCAI 2017 Workshop
The MICCAI community needs data with known ground truth to develop, evaluate, and validate image analysis and reconstruction algorithms. Since synthetic data are ideally suited for this purpose, over the years, a full range of models underpinning image simulation and synthesis have been developed: (i) simplified mathematical models to test segmentation and registration algorithms; (ii) detailed mechanistic models (top-down), which incorporate priors on the geometry and physics of image acquisition and formation processes; and (iii) complex spatio-temporal computational models of anatomical variability, organ physiology, or disease progression. Recently, cross-fertilisation between image computing and machine learning gave rise to data-driven, phenomelogical models (bottom-up) that stem from learning directly data associations across modalities, resolutions, etc. With this, not only the application scope has been expanded but also the underlying model assumptions have been refined to increasing levels of realism.
This half-day workshop aims to stimulate the discussion and research in simulation and synthesis approaches, invite new ideas on how to best characterise and evaluate these techniques, and ultimately help bring these two synergistic perspectives closer together.