Manifolds in Medical Imaging: Metrics, Learning and Beyond

September 10, 2008

In modern medical image data, manifolds arise at varying scales. At one extreme, complete 3D data sets lie along manifolds parameterized by (for example) patient breathing and heartbeat patterns, or by confounding variables such as parameters or templates used in an image warping algorithm. At the other extreme, measurements taken at each voxel in multi-parametric MR images lie along locally defined manifolds that reflect nonlinear relationships among various MRI measurements on a voxel.

Discovering, visualizing and exploiting the structure of these manifolds supports the ability to select image-derived attributes that are informed by the structure of the underlying manifold. This offers fundamentally new tools for image registration, segmentation, visualization, reconstruction, and classification of data volumes. This workshop aims to bring together researchers in computer science, applied mathematics, statistics and medical imaging to present state of the art developments in this area.

Program

Submissions are encouraged in (but not limited to) the following topics:

General Chairs:
Robert Pless, Washington University in St. Louis
Christos Davatzikos, University of Pennsylvania

Area Chairs:
Richard Souvenir, University of North Carolina at Charlotte
Anders Brun, Centre for Image Analysis, Uppsala, Sweden

Program Committee:
Ghassan Hamarneh, Simon Fraser University
Andrew Hope, University of Toronto
Rasmus Larsen, Technical University of Denmark
Frangois G. Meyer, University of Colorado
Xavier Pennec, INRIA
Ragini Verma, University of Pennsylvania
Carl-Fredrik Westin, Harvard
Axel Wismueller, Rochester
Important Deadlines

Submission: June 5
Acceptance: July 7 (sorry for the delay)
Final versions: August 15

Paper Submission
  • Please use the MICCAI author kit to format the papers, maximum length of 8 pages.
  • Submitted in PDF format
  • Color illustrations in the PDF are not subject to fees
  • Accepted papers will be published in the Insight Journal (online).
Papers can be submitted at any time by e-mail'ing the PDF to: pless@cs.wustl.edu with a subject line of "MMI submission".