Morphometric Gaussian Process for Landmarking on Grey Matter Tetrahedral Models.

ABSTRACT: High-dimensional manifold modeling increases the precision and performance of cortical morphometry analysis by densely sampling on the grey matters. But this also brings redundant information and increased computational burden. Gaussian process regression has been used to tackle this problem by learning a mapping to a low-dimensional subspace. However, current methods may not take relevant morphometric properties, usually measured by geometric features, into account, and as a result, may generate morphometrically insignificant selections. In this paper, we propose a morphometric Gaussian process (M-GP) as a novel Bayesian model on the gray matter tetrahedral meshes. We also implement an M-GP regression landmarking algorithm as a manifold learning method for non-linear dimensionality reduct

SUBMITTER: Fan Y 

PROVIDER: S-EPMC8112202 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature