Skip to content. | Skip to navigation


Personal tools

This is SunRain Plone Theme
You are here: Home / Training / 2013 / 4D Dynamics and Statistics by Spatio-Temporal 3D Image and Shape Analysis

4D Dynamics and Statistics by Spatio-Temporal 3D Image and Shape Analysis

Prof. Guido Gerig, Scientific Computing and Imaging Institute (SCI), School of Computing, University of Utah, USA

4D Dynamics and Statistics by Spatio-Temporal 3D Image and Shape Analysis

Guido Gerig, Ph. D.

  • CREATE-MIA Event
  • Seminar
When Nov 15, 2013
from 02:00 PM to 03:30 PM
Where McConnell Engineering MC437
Attendees All CREATE-MIA Trainees and anyone interested in learning more about the subject.
Add event to calendar vCal

Rapid advances in image acquisition and shape capturing technology provide continuous or time-discrete 3D volumetric images and/or surfaces, motivated by the notion that dynamic spatiotemporal changes may provide information not available from snapshots in time. The segmentation of 4D data embedding time-varying objects and analysis 4D surfaces requires a new class of methods and tools to make use of the inherent correlation and causality of repeated acquisitions. This talk will discuss progress in the development of advanced 4D image analysis methodologies that carry the notion of linear and nonlinear regression, now applied to complex, high-dimensional data such as images, image-derived shapes and structures, or a combination thereof. Methods include joint segmentation of serial 3D data by 4D segmentation enforcing temporal consistency. We will also address concepts for regression of 4D shapes from time-discrete data, and work in progress towards statistical analysis of 4D shape trajectories via diffeomorphic flows. We will demonstrate that statistical concepts of longitudinal data analysis such as linear and nonlinear mixed-effect modeling (NLME), commonly applied to univariate or low-dimensional data, can be extended to structures and shapes modeled from serial image data.

Our research is driven by challenging medical image analysis problems. Clinical assessment routinely uses terms such as development, growth trajectory, aging, degeneration, disease progress, recovery or prediction. This terminology inherently carries the aspect of dynamic processes, suggesting that snapshots in time and cross-sectional analysis are not sufficient. We will show examples from ongoing clinical studies such as analysis of early brain growth in healthy and at-risk subjects, and of understanding neurodegeneration in normal aging and Huntington’s and Alzheimer’s disease. However, methodologies are generic and may find applications in a wide range of areas where we would like to extract spatiotemporal models from dynamic image series.

Bibliography ( and Biography

M. Prastawa, S.P. Awate, G. Gerig. “Building Spatiotemporal Anatomical Models using Joint 4-D Segmentation, Registration, and Subject-Speci fic Atlas Estimation,” In Proceedings of the 2012 IEEE Mathematical Methods in Biomedical Image Analysis (MMBIA) Conference, IEEE Explorer. Jan. 2012.

J. Fishbaugh, M. Prastawa, S. Durrleman, G. Gerig. “Analysis of Longitudinal Shape Variability via Subject Specific Growth Modeling,” In Proceedings of MICCAI 2012, Med Image Comput Comput Assist Interv. 2012;15(Pt 1):731-8.

M. Datar, P. Muralidharan, A. Kumar, S. Gouttard, J. Piven, G. Gerig, R.T. Whitaker, P.T. Fletcher. “Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy,” In Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, Lecture Notes in Computer Science, Vol. 7570, Springer Berlin / Heidelberg, pp. 76--87. 2012. 
ISBN: 978-3-642-33554-9  DOI: 10.1007/978-3-642-33555-6_7

N. Sadeghi, M. Prastawa, P.T. Fletcher, J. Wolff, J.H. Gilmore, G. Gerig. “Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain,” In NeuroImage, Neuroimage. 2013 Mar;68:236-47.  DOI: 10.1016/j.neuroimage.2012.11.040

B. Wang, M. Prastawa, S.P. Awate, A. Irimia, M.C. Chambers, P.M. Vespa, J.D. Van Horn, G. Gerig.“Segmentation of Serial MRI of TBI patients using Personalized Atlas Construction and Topological Change Estimation,” In Proceedings of IEEE ISBI 2012, pp. 1152--1155. 2012.  DOI: 10.1109/ISBI.2012.6235764

A. Irimia, B. Wang, S. Aylward, M. Prastawa, D. Pace, M. Niethammer, G. Gerig, D.A. Hovda, R. Kikinis, P.M. Vespa, J.D. Van Horn. “Multimodal neuroimaging of structural pathology and neuroconnectivity in traumatic brain injury: toward personalized outcome prediction,” In NeuroImage: Clinical, Vol. 1, No. 1, Elsvier, pp. 1—17, /12, 2012

« September 2017 »
Funded by NSERC

Funding provided by NSERC