Brain Population Analysis with Manifold Models
In many neuroimage applications a summary or representation of a
population of brain images is needed. A common approach is to build a
template, or atlas, that represents a population. Recent work introduced
clustering based approaches, which in a data driven fashion, compute
multiple templates Each template represents a part of the population.
In a different direction, researcher proposed kernel-based regression of
brain images with respect to an underlying parameter. This yields a
continuous curve in the space of brain images that estimates the
conditional expectation of a brain image given the parameter. A natural
question that arises based on these investigations is can the space
spanned by a set of brain images be approximated by a low-dimensional
manifold? In other words, how effectively can a low-dimensional,
nonlinear model represent the variability in brain anatomy.
We adapt the kernel map manifold approach to work on a shape space
based on geometric coordinate transformations. This allows to measure
shape changes in a low dimensional Euclidean space.We apply this method
to the OASIS and ADNI brain database. We show that the learned manifold
provides a good fit in terms of projection distance and as a proxy for
statistical analysis. We perform linear regression of the learned
manifold coordinates with several clinical parameters. This provides
strong evidence that the proposed manifold representation of brain image
data sets captures important clinical trends.
Related Publications
Samuel Gerber, Tolga Tasdizen, Sarang Joshi, Ross Whitaker,
"On the Manifold Structure of the Space of Brain Images",
In Proceedings of the 2009 International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI 2009) [pdf]
Samuel Gerber, Tolga Tasdizen, Thomas P Fletcher, Ross Whitaker,
"Manifold Modeling for Brain Population Analysis", Medical Image
Analysis, Volume 14, Issue 5, Special Issue on the 12th International
Conference on Medical Image Computing and Computer-Assisted Intervention
(MICCAI) 2009, October 2010, Pages 643-653, [pdf] Best Paper Award