Preface

摘要

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: (a) simplified mathematical models to test segmentation and registration algorithms; (b) detailed mechanistic models (top-down), which incorporate priors on the geometry and physics of image acquisition and formation processes; and (c) complex spatiotemporal computational models of anatomical variability, organ physiology, or disease progression. Recently, cross-fertilization between image computing and machine learning gave rise to data-driven, phenomenological 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.