Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data.

Characterizing the cellular architecture (cytoar-chitecture) of tissues in the nervous system is critical for modeling disease progression, defining boundaries between brain regions, and informing models of neural information processing. Extracting this information from anatomical data requires the expertise of trained neuroanatomists, and is a challenging task for inexperienced analysts. To address this need, we present an unbiased, automated method to estimate cellular density of retinal and neocortical datasets. Our approach leverages the fact that within retinal and neurocortical datasets, cells are organized into "layers" of constant density to approximate cytoarchitecture with a small number of known basis elements. We introduce methods for patch extraction, cell detection, and sparse approximation of inhomogeneous Poisson processes to differentiate changes in cellular densities and detect layers. Our results demonstrate the feasibility of using automation to reveal the cytoarchitecture of large-scale biological samples.