[Insight-users] IEEE-TMI Special Issue on Computational Neuroanatomy

[Paul Yushkevich]

Special Issue on Computational Neuroanatomy
IEEE Transactions on Medical Imaging

James C. Gee, Paul M. Thompson
Guest Editors

Spurred by powerful imaging methods that assess the human brain in vivo,
computational neuroanatomy has emerged over the last decade as an important
field of applied science.  This new field is greatly advancing medical
research,  and basic biological science and clinical practice.  Detailed
imaging-assisted studies of human development and disease are being
performed. Structural-functional research is similarly supported by
underlying automated image registration programs that align subject studies
within a documented structural space. Development of detailed anatomical
models, atlases, and templates for surgical planning and teaching is another
valuable application.  Image segmentation algorithms are also vital to
identify structures in the brain automatically, for subsequent volumetric
measurement and shape analysis.

Quantitative analysis of the brain is especially complex because of the need
to measure, compare, and integrate data from the cerebral cortex.  Cortical
models and maps, and tools to analyze cortical data, are accelerating our
understanding of natural and abnormal variability in brain structure and
function as well as in psychiatric and neurodegenerative diseases. The
cortex plays the key role in consciousness and cognition and changes
structurally and functionally in psychiatric and neurodegenerative
conditions.  The most striking feature of the cerebral cortex is its
convoluted shape, which is considered key to human intelligence.  The major
sulci and gyri on the surface are largely conserved between individuals,
making them useful landmarks for morphometric comparisons.  The deep,
primary sulcal folds also delimit the boundaries of major functional
regions. Detailed studies of the cortical surface are revealing evolutionary
differences between species, relating genotype to phenotype, shedding light
on the connection between the paradigms of structure and function, and
aiding drug development.

A central factor in the success and increasingly widespread application of
computational neuroanatomy approaches in medicine has been the development
of sophisticated mathematical and algorithmic methods for extracting and
modeling clinically significant and scientifically important information
from image data.  This Special Issue will highlight new research directions
in computational neuroanatomy by collecting selected papers in all relevant
areas including, but not limited to, the following topics:

·      Pattern theory/recognition
·      Image segmentation/classification, including automated identification
of brain structures
·      Registration, including nonlinear and cross-modality approaches
·      Deformable geometry and surface-based anatomical modeling
·      Shape
·      Longitudinal analysis, i.e. modeling of dynamic brain changes
·      Computer-aided diagnosis
·      Morphometry and structure-function correlations
·      Statistical analysis of structure comparing groups or clinical
populations

The IEEE Transactions on Medical Imaging seeks high-quality original
research papers for this Special Issue.  Authors should submit their
manuscripts electronically, by the deadline below, through the IEEE
Manuscript Central Office (http://mc.manuscriptcentral.com/tmi-ieee)
following the T-MI Instructions for Authors and indicating in the Author
Comments to the Editor-in-Chief that the manuscript be considered for the
special issue on Computational Neuroanatomy.  Authors intending to submit
articles are encouraged to discuss their submissions with the Guest Editors
to determine suitability for this Special Issue.

Schedule: Submission of manuscripts, June 1, 2006; Acceptance/rejection
notification, September 1, 2006; Revised manuscripts due, November 1, 2006;
Publication of Special Issue, March 2007

James C. Gee, PhD
University of Pennsylvania
Department of Radiology
Penn Image Computing and Science Laboratory
3600 Market Street, Suite 370
Philadelphia, PA 19104, USA
+1 215 662 7109
gee at mail.med.upenn.edu

Paul M. Thompson, PhD
UCLA School of Medicine
Department of Neurology
Laboratory of Neuro Imaging
635 Charles E. Young Drive South, Suite 225E
Los Angeles, CA 90095-7332, USA
+1 310 206 2101
thompson at loni.ucla.edu



--
Paul A. Yushkevich, Ph.D.
Research Assistant Professor
Penn Image Computing and Science Laboratory (PICSL)
Department of Radiology,
University of Pennsylvania
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