Computational anatomy to assess longitudinal trajectory of brain growth

Update item information
Publication Type journal article
School or College <blank>
Department <blank>
Creator Gerig, Guido
Other Author Davis, B.; Lorenzen, P.; Xu, Shun; Jomier, M.; Piven, J.; Joshi, S.
Title Computational anatomy to assess longitudinal trajectory of brain growth
Date 2006-01-01
Description This paper addresses the challenging problem of statistics on images by describing average and variability. We describe computational anatomy tools for building 3-D and spatio-temporal 4-D atlases of volumetric image data. The method is based on the previously published concept of unbiased atlas building, calculating the nonlinear average image of a population of images by simultaneous nonlinear deformable registration. Unlike linear averaging, the resulting center average image is sharp and encodes the average structure and geometry of the whole population. Variability is encoded in the set of deformation maps. As a new extension, longitudinal change is assessed by quantifying local deformation between atlases taken at consecutive time points. Morphological differences between groups are analyzed by the same concept but comparing group-specific atlases. Preliminary tests demonstrate that the atlas building shows excellent robustness and a very good convergence, i.e. atlases start to stabilize after 5 images only and do not show significant changes when including more than 10 volumetric images taken from the same population.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 1041
Last Page 1047
Language eng
Bibliographic Citation Gerig, G., Davis, B., Lorenzen, P., Xu, S., Jomier, M., Piven, J., & Joshi, S. (2006). Computational anatomy to assess longitudinal trajectory of brain growth. roceedings of The Third International Symposium on 3D Data Processing, Visualization, and Transmission, 1047-7.
Rights Management (c) 2066 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 1,934,007 bytes
Identifier uspace,19270
ARK ark:/87278/s6mp8cch
Setname ir_uspace
Date Created 2015-02-09
Date Modified 2015-02-09
ID 712807
Reference URL https://collections.lib.utah.edu/ark:/87278/s6mp8cch
Back to Search Results