SASHIMI 2019: Simulation and Synthesis in Medical Imaging

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Overview

SASHIMI 2019: A MICCAI 2019 Workshop

The MICCAI community needs data with known ground truth to develop, evaluate, and validate computerised image analytic tools, as well as to facilitate clinical training. 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) machine and deep learning methods based on generative models, (b) simplified mathematical models to test segmentation, tracking, restoration, and registration algorithms; (c) detailed mechanistic models (top–down), which incorporate priors on the geometry and physics of image acquisition and formation processes; and (d) complex spatio-temporal computational models of anatomical variability, organ physiology, morphological changes in tissues or disease progression.

The goal of the Simulation and Synthesis in Medical Imaging (SASHIMI) workshop is to bring together all those interested in such problems for invigorating research, discussing current approaches, and stimulating new ideas and scientific directions in this field. The objectives are to: (a) hear from invited speakers in the areas of transfer learning, generative adversarial networks, variational auto encoders, and biophysical models, and cross-fertilize across these fields; (b) bring together experts of image synthesis to raise the state of the art; and (c) identify challenges and opportunities for further research. We also would like to identify the suitable approaches to evaluate the plausibility of synthetic data and to collect benchmark data that can help with the development of future algorithms.

Topics

Topics of interest include, but are not limited to, the following:

  • Fundamental methods for image-based biophysical modeling and image synthesis
  • Biophysical and data-driven models of disease progression, or organ development, motion and deformation
  • Biophysical and data-driven models of image formation and acquisition
  • Virtual cell imaging
  • Segmentation / registration across or within modalities to aid the learning of model parameters
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  • Imaging protocol harmonization approaches across imaging systems, sites and time points
  • Image synthesis for normalization and spatio-temporal intensity correction
  • Cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis
  • Simulation and synthesis from large-scale image databases
  • Automated techniques for quality assessment of simulations and synthetic images
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  • Image synthesis in high dimensional spaces (vectors, tensors, spatio-temporal features, etc.)
  • Handling uncertainty and incomplete data via simulation and synthesis techniques
  • Evaluation and benchmarking of state of-the-art approaches in simulation and synthesis
  • Normative and annotated datasets for benchmarking and learning models
  • Novel ideas on evaluation metrics and methods in image-based simulation and image synthesis
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  • Applications of image synthesis in super resolution imaging and multi/cross-scale regression
  • Applications of image synthesis and simulation in medical image registration and segmentation
  • Applications of image synthesis and simulation in image denoising and information fusion
  • Applications of synthesis and simulation to image reconstruction from sparse data or sparse views
  • Applications of image and data synthesis to real-time simulation of biophysical properties
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Organization committee

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University of Leeds, UK
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CNRS, Brain and Spine Institute, France
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Masaryk University, Czech Republic