Particle based shape regression of open surfaces with applications to developmental neuroimaging.
Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. In this paper we propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The statistical significance of the dependence is evaluated using permutation tests designed to estimate the likelihood of achieving the observed statistics under numerous rearrangements of the shape parameters with respect to the explanatory variable. We demonstrate the method on synthetic data and provide a new results on clinical MRI data related to early development of the human head.
Datar
Manasi
M
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.
Cates
Joshua
J
Fletcher
P Thomas
PT
Gouttard
Sylvain
S
Gerig
Guido
G
Whitaker
Ross
R
D000375:Aging / Q000473:pathology*; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique