SASHIMI 2021 – 6th Workshop on Simulation and Synthesis in Medical Imaging
Held in conjunction with MICCAI 2021

Website: 2021.sashimi-workshop.org
Email: [log in to unmask]

Submission deadline: June 25


Scope of the Workshop:
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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. In order to make their research transparent and reproducible, authors are encouraged to make the code of the proposed methods and algorithms freely available for the community, e.g. via GitHub. This year, we introduce a reproducibility award which will be given to the paper with publicly available, well documented, and easy to use source code. This encouragement is however not obligatory as there may appear situations (security reasons, project rules, …) that may disallow publishing the source code.

This year, all papers submitted to SASHIMI will be split into three categories: papers introducing new methods, application-oriented papers, and demos. During the submission process, the authors will be asked to choose one of the following options:
+ Method-oriented paper: The main emphasis during the review process will be put on the novelty, correctness of the presented method, and proper validation of the results. The authors are expected to submit a full-length paper which will be (upon acceptance) included in the conference proceedings. Optionally, the authors of method-oriented papers are welcome to demonstrate their new methods during the demo session.
+ Application-oriented paper: The main focus will be put on the novel application of an existing theoretical image synthesis method and the data which the given method is applied to. The data should be described in detail. For new data collected, the collection process should be described (experimental setup, device(s) used, image acquisition parameters, subjects/objects involved, instructions to annotators, methods for quality control). For existing datasets, citations, as well as descriptions if they are not publicly available, should be specified. If public, a link to a downloadable version of the dataset should be included. The authors are expected to submit a full-length paper which will be (upon acceptance) included in the conference proceedings. Optionally, the authors of application-oriented papers are welcome to demonstrate their results during the demo session.
+ Demo abstract: An already published journal/conference paper or patent may be advertised during the demo session. The authors are expected to submit an extended abstract (max 2 pages of LNCS format excluding references) that will not be included in the conference proceedings. The aim of the abstract will be to draw attention of the participants to some particular method and to demo it.


Submission:
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SASHIMI 2021 will accept 10-page papers (LNCS-Springer format), similar to the MICCAI format. Selected papers will be published in a Lecture Notes in Computer Science volume published by Springer. You can submit your paper here: https://ocs.springer.com/ocs/home/SASHIMI2021


Important dates:
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Submission deadline: June 25
Notification of decision: July 23
Camera ready papers due: August 6
Demo abstract submission due: August 6
Workshop event: September 27 or October 1

Topics:
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- Fundamental methods for image-based biophysical modelling and image synthesis
- Biophysical and data-driven models of disease progression, organ development, motion and deformation, image formation and acquisition
- Virtual cell imaging 
- Segmentation/registration across or within modalities to aid model parameter learning
- Imaging protocol harmonization approaches across imaging systems, sites and time points
- Cross-modality image synthesis
- Image synthesis for normalization and spatio-temporal intensity correction
- Simulation and synthesis from large-scale databases
- Machine and deep learning techniques in image simulation and synthesis
- Handling uncertainty and incomplete data via simulation and synthesis techniques
- Automated techniques for quality assessment of simulations and synthetic images
- Evaluation and benchmarking of state-of-the-art approaches in simulation and synthesis
- Novel ideas on evaluation metrics and methods in image-based simulation and image synthesis
- Normative and annotated datasets for benchmarking and learning models
- Applications of image synthesis/simulation in super resolution imaging and multi/cross-scale regression, registration, segmentation, denoising, fusion reconstruction and real-time simulation of biophysical properties


Organising Committee:
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David Svoboda, Masaryk University, Czech Republic
Ninon Burgos, CNRS - Paris Brain Institute, France
Jelmer Wolterink, University of Twente, The Netherlands
Can Zhao, Nvidia, Johns Hopkins University, USA

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