Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks.

ABSTRACT: Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to pre

SUBMITTER: Nejatbakhsh A 

PROVIDER: S-EPMC10358289 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature