MICCAI
2010 WORKSHOP STIA’10: Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data |
Organizers:
Guido
Gerig, University of Utah (gerig@sci.utah.edu)
Thomas
Fletcher, University of Utah (fletcher@sci.utah.edu)
Xavier Pennec, INRIA Sophia Antipolis (Xavier.Pennec@sophia.inria.fr )
Important Dates:
June
13th, 2010 Full paper
submissions submissions (Submission).
July 18th,
2010 Notification of acceptance
(please note change of date)
August 18th,
2010 Camera ready papers due
Sept. 24 Workshop MICCAI
2010 F-W4
BEST PAPER AWARDS (4 winners were selected by the
audience among the 16 presentations):
·
DTI Longitudinal Atlas Construction as an Average of Growth Models, Gabriel Hart (UNC), Yundi Shi (UNC) , Hongtu Zhu
(UNC) , Mar Sanchez (Emory University) , Martin Styner
(UNC), Marc Niethammer (UNC Chap. H)
·
4D registration of serial brain's MR images: a robust measure of
changes applied to Alzheimer's disease, Marco
Lorenzi (INRIA Sophia Antipolis
/ Asclepios team) , Nicholas Ayache
(INRIA Sophia Antipolis / Asclepios
team) , Giovanni Frisoni (IRCCS Fatebenefratelli),
Xavier Pennec (INRIA, Asclepios
)
·
Group-wise Spatio-Temporal Registration
and Segmentation of Fetal Cortical Surface Development, Georg Langs (MIT), Gregor
Kasprian (Medical University of Vienna), Eva Dittrich (Medical University of Vienna) , Mario Bittner
(Medical University of Vienna) , Peter Brugger
(Medical University of Vienna), Daniela Prayer (Medical University of Vienna)
·
A spatio-temporal model for joint
segmentation and registration of cardiac cine MR images, An Elen (K.U.Leuven),
Dirk Loeckx (K.U.Leuven) ,
Jan Bogaert (K.U.Leuven) , Frederik Maes (K.U.Leuven), Paul Suetens (K.U.Leuven)
Rationale:
Clinical imaging increasingly makes use of
longitudinal image studies to examine subject-specific changes due to
pathology, intervention, therapy, neurodevelopment, or neurodegeneration.
Moreover, dynamic organ changes as seen in cardiac imaging or functional
changes as measured in perfusion imaging, just to name a few, by definition
result in time-series image data presenting volumetric image data over time.
Expressions such as development, degeneration, disease progress, recovery, monitoring or heart cycle inherently carry the aspect of a
dynamic process – suggesting that imaging at multiple time points may be
applied. The detection and characterization of changes from baseline due to
disease, trauma, or treatment require novel image processing and visualization
tools for qualitative and quantitative assessment of change trajectories. Whereas
longitudinal analysis of scalar data is well known in the statistics community,
its extension to high-dimensional image data, shapes or functional changes
poses significant challenges. Cross-sectional analysis of longitudinal data
does not provide a model of growth or change that considers the inherent
correlation of repeated images of individuals, nor does it tell us how an
individual patient changes relative to a change over time of a comparable
healthy or disease-specific population, an aspect which is highly relevant to
decision making and therapy planning. Although successful early results were
presented for image regression in aging studies, such standard regression is
not optimal for longitudinal data because repeated observations from the same
individual would ignore the correlation between repeated measurements and thus
violate the Gauss-Markov assumption of independence. Moreover, individual
change trajectories often need to be interpreted in relationship to a
population growth model, which in turn is the hidden group model given a
representative set of individual trajectories, and require a common framework
based on the use of hierarchical linear
models (HLM). Other typical driving applications are concerned with registration
of serial data of the cardiac cycle, sampled at different time points, or
measuring object shape changes via shape regression, both requiring new image
registration and modeling approaches.
The special nature of
longitudinal or repeated, time-series data of individual subjects, with the
inherent correlation of structure and function across the sequence of images,
resulted in the development of a variety of new image processing and analysis
approaches tackling the challenging issues of registration, segmentation and
analysis in the presence of geometric and contrast changes over time. New
methodologies are rapidly evolving, often focusing on the specific application
at hand.
This workshop will
cover a comprehensive discussion of multiple approaches and new advances for spatio-temporal image processing of longitudinal image data
but also aims at a dialogue to define the generic nature of algorithms,
methods, modeling approaches, and statistical analysis for optimal analysis of
such data. This in turn will lead to a discussion on solutions achieved so far,
summary of the state-of-the-art and open issues to be tackled in future
research.
Goals of the workshop are the
following:
The primary focus of this workshop will be on
the generic nature of algorithms, methods, modeling approaches and
statistical analysis. We are specifically looking for papers that introduce novel
methodology for spatiotemporal analysis of images. We will invite paper
submissions related to the following topics:
Provisional list of
topics:
Workshop
Format:
We plan to start with a series of invited talks
presenting major representative areas covering clinical motivation and
methodological aspects of longitudinal imaging and image analysis. We will then
continue with oral presentations of high-quality research selected from
submitted papers (reviewed and selected by a paper review committee). Other
paper submissions will be selected for short poster presentations. The
presentations will be moderated by a session chair and will be followed by a
joint discussion on methodologies developed so far and challenging open issues
to be tackled in the future. We also plan to compile a list of freely available
software and to collect a list of publicly available image databases of
longitudinal image data to serve as testbeds for the
scientific community. We will further distribute a list of books, book chapters
and scholarly articles relevant to the specific topic. Presentations and
associated papers will be available electronically to registered workshop
attendees. Would the quality of submitted papers be highly innovative and
original, we might plan to invited the best papers as
a special issue in a scientific journal.
To our knowledge, this
will be the first MICCAI workshop on the topic spatio-temporal image analysis, but related to a seminal tutorial on
“Detection and Quantification of Evolving Processes in Medical Images”
organized by Nicholas Ayache at MICCAI 2004. The topic
is initiated by the need for information exchange and brainstorming w.r.t. this new, rapidly evolving image analysis
sub-discipline.
Participants will get
pdf copies of papers and eventually presentations
(ready on CD at workshop).
Target
audience:
There
is a rapidly growing interest in the analysis of time-series data. Recent
examples are the very successful MICCAI 2008 and 2009 workshops on image
analysis of the early developing brain, where a modeling of change trajectories
of growth and brain maturation were key topics. 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 or , 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 interesting 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 of existing methods, procedures and problem
solutions. We expect to create discussions between potential users and
researchers in order to inform about existing technology and to lay the ground
for future research.