Call for Papers: SASHIMI - Simulation and Synthesis in Medical Imaging

SASHIMI: Simulation and Synthesis in Medical Imaging

A MICCAI 2016 Workshop
October 21, 2016, Athens, Greece
Website: http://www.cistib.org/sashimi
In conjunction with MICCAI 2016 ( http://miccai2016.org/en/ )

Important Dates:

Submission due:    June 10
Notification of acceptance:    July 22
Workshop event:    October 21

Scope of the Workshop:

The MICCAI community has always been close to the idea of creating simulated or synthetic data to understand, develop, assess, and validate image analysis and reconstruction algorithms. From very basic digital phantoms all the way up to very realistic in silico models of medical imaging and physiology, our community has progressed enormously in terms of the available techniques and their applications. For instance, mechanistic models (imaging simulations) emulating the geometrical and physical aspects of the acquisition process have been used now for a long time. Advances on computational anatomy and physiology have further enhanced the potential of such simulation platforms by incorporating structural and functional realism to the simulations that can now account for complex spatio-temporal dynamics due to changes in anatomy, physiology, disease progression, patient and organ motion, etc. just to name a few. More recently, developments in machine learning together with the growing availability of ever-larger scale databases have provided the theoretical underpinning and the practical data access to develop phenomelogical models (image synthesis) that learn models directly from data associations across subjects, time, modalities, resolutions, etc. These techniques may provide ways to address challenging tasks in medical image analysis like cross-cohort normalization, image imputation in the presence of missing or corrupted data, transfer of knowledge across imaging modalities, views or domains. To this date, however, these two main research avenues (simulation and synthesis) remain pretty much independent efforts in spite of sharing common challenges. This satellite workshop aims to provide a state-of-the-art and integrative perspective on simulation and synthesis in medical imaging for the purpose of invigorating research and stimulating new ideas on how to build theoretical links, practical synergies, and best practices between these two research directions.

Topics:

Specific 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, organ motion and deformation, image formation and acquisition
Segmentation/registration across or within modalities to aid the learning of model parameters
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 databases
Automated techniques for quality assessment of simulations and synthetic images
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
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

Invited Speaker:

Prof. Christoph Lampert, Institute of Science and Technology Austria (IST Austria)

Further Information and Submission Guidelines:

Workshop proceedings will be published as a Lecture Notes in Computer Science volume (Springer).
Additional and up to date information about the workshop, author instructions, submission guidelines, and our invited speaker are available at: http://www.cistib.org/sashimi/

Workshop Organization:

Workshop Chairs:

Sotirios A Tsaftaris, University of Edinburgh, UK

Alejandro F Frangi, University of Sheffield, UK

Jerry L Prince, Johns Hopkins University, USA

Organising committee:

Ali Gooya, University of Sheffield, UK

Ilkay Oksuz, Yale University and IMT Lucca, Italy

Email to contact the organizers: This email address is being protected from spambots. You need JavaScript enabled to view it.

Program Committee

Martino Alessandrini, University of Bologna, Italy

Daniel Alexander, University of College London, UK

Leandro Beltrachini, University of Sheffield, UK

M. Jorge Cardoso, University of College London, UK

Tim Cootes, University of Manchester, UK

Jan D'Hooge, KU Leuven, Belgium

Christos Davatzikos, University of Pennsylvania, USA

Marleen de Brujine, Erasmus University Medical Center, The Netherlands

Mathieu De Craene, Philips Research, France

Herve Delingette, Inria Sophia Antipolis, France

Dimitrios Fotiadis, University of Ioannina, Greece

Ali Gooya, University of Sheffield, UK

Daniel Herzka, John Hopkins University, USA

Ender Konukoglu, Martinos Center for Biomedical Imaging, USA

Sebastian Kozerke, Institute for Biomedical Engineering, ETH Zurich, Switzerland

Hervé Liebgott, CREATIS, France

David Liu, Siemens Medical Solutions, USA

Nassir Navab, TU Munich, Germany

Hien V Nguyen, Siemens Corporate Research. USA

Xenios Papademetris, Yale University, USA

Dzung L Pham, National Institutes of Health, USA

Adityo Prakosa, John Hopkins University, USA

Anqi Qiu, National University of Singapore, Singapore

Snehashis Roy, National Institutes of Health, USA

Daniel Rueckert, Imperial College, UK

Maxime Sermesant, Inria Sophia Antipolis, France

Ling Shao, Northumbria University, UK

Dinggang Shen, University of North Carolina, USA

François Varray, CREATIS, France

Devrim Unay, Izmir University of Economics, Turkey

Alistair Young, The University of Auckland, New Zealand

Kevin Zhou, Siemens Corporate Research, USA