STIA 2014 - 3rd International MICCAI Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA'14)

The proposed workshop aims to be the follow-up of the first and second international workshops on Spatiotemporal Image Analysis (STIA) for Longitudinal and Time-Series Image Data. The first workshop was held in conjunction with MICCAI 2010 in Beijing. The first STIA’10 workshop was a success with 58 full paying attendees in the advanced registrations, 8 podium speakers and 8 poster presentations selected from a total of about 25 submitted papers. Papers were sent from a large variety of institutions from all geographic regions. The 2nd STIA’12 workshop at MICCAI’12 in Nice showed about 70 attendees. As a novelty for STIA, the 13 selected papers (8 oral, 5 posters), reviewed by 25 international experts in this field, were invited to be published in a special Springer Verlag LNCS 7570 proceedings.
The obviously increasing interest of the MICCAI community in a methodology-oriented workshop on longitudinal image data analysis supports, in our view, the organization of a second workshop on the same topic. There seems a clear need for information exchange and brainstorming with respect to this new and rapidly evolving image analysis subdiscipline. Such a workshop focusing more on theoretical and methodological aspects rather than application-driven research would also complement and augment previous workshops dedicated to very specific applications such as “early brain development”. A large numbers of papers have been published in the main journals and conference proceedings over the last two years (for instance, last MICCAI meeting in 2011 had a session dedicated to this topic). The organization of such a workshop on a regular basis would help to establish the MICCAI meetings as a key forum to discuss novel theoretical and methodological advances, to foster collaborations among the specialists in the field, and to provide profound education and information about the state-of-the-art to researchers novel to this topic.
Target Audience:
There is a rapidly growing interest in the analysis of time-series data. Recent examples are the very successful MICCAI 2008, 2009 and 2011 workshops on image analysis of the early developing brain, where a modeling of brain growth and brain maturation were key topics but with the main focus on a very specific application domain. Our objective is to move beyond such a specific application domain with the proposed workshop by focusing on common underlying methodologies to analyze time-series data. The increased focus on personalized medicine or subject-specific analysis includes processing of time-series data such as pre-/post-therapy or modeling of lesion evolution via parameterized models. Clinical studies of aging, e.g., include series of follow-up scans to stage and model the effect of aging and to determine the onset of accelerated degeneration. Rapidly evolving advanced imaging technology can routinely measure volumetric data in short time intervals, creating 4D datasets that require new, efficient processing, visualization, and quantitative analysis techniques.
The target audience will therefore be researchers interested in or already involved in research and development of methods for studying growth or change patterns in longitudinal and time-series image data. This workshop aims at contributing to a fundamental understanding of data, processing methodology and statistical concepts but also to a review and discussion of existing methods, procedures and problem solutions. We expect to create discussions between researchers, developers and potential users in order to inform about existing technology, image databases for testing and comparison, and to lay the ground for future research.
Sponsorship:
We are thankful to ICM (Hôpital Pitié Salpêtrière, Brain and Spine Institute, Pariswww.icm-institute.org) and SCI (Scientific Computing and Imaging Institute, Utah (www.sci.utah.edu) for sponsoring this workshop.The NA-MIC consortium (www.na-mic.org) funded by NIH is acknowledged for providing scientific and engineering support.