Title: Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation,
Abstract: Probabilistic atlases provide essential spatial contextual information for
image interpretation, Bayesian modeling, and algorithmic processing. Such
atlases are typically constructed by grouping subjects with similar demographic
information. Importantly, use of the same scanner minimizes inter-group
variability. However, generalizability and spatial specificity of such
approaches is more limited than one might like. Inspired by Commowick
"Frankenstein's creature paradigm" which builds a personal specific anatomical
atlas, we propose a data-driven framework to build a personal specific
probabilistic atlas under the large-scale data scheme. The data-driven
framework clusters regions with similar features using a point distribution
model to learn different anatomical phenotypes. Regional structural atlases and
corresponding regional probabilistic atlases are used as indices and targets in
the dictionary. By indexing the dictionary, the whole brain probabilistic
atlases adapt to each new subject quickly and can be used as spatial priors for
visualization and processing. The novelties of this approach are (1) it
provides a new perspective of generating personal specific whole brain
probabilistic atlases (132 regions) under data-driven scheme across sites. (2)
The framework employs the large amount of heterogeneous data (2349 images). (3)
The proposed framework achieves low computational cost since only one affine
registration and Pearson correlation operation are required for a new subject.
Our method matches individual regions better with higher Dice similarity value
when testing the probabilistic atlases. Importantly, the advantage the
large-scale scheme is demonstrated by the better performance of using
large-scale training data (1888 images) than smaller training set (720 images).