Kathy Olson

TL;DR: This work has shown that tight clustering of nuclei in 3D confocal microscope images is a common source of segmentation error, and a compelling need to minimize these errors for constructing highly automated scoring systems.

TL;DR: The multi‐model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations.

TL;DR: To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed.

TL;DR: A comprehensive validation of data generated by the automated software against manual quantification by human experts on hippocampal and parietal cortical regions is presented, suggesting that the software will generalize well to multiple brain areas and eventually to large-scale brain analysis.

TL;DR: Though the networks of cells that participate in behavior or seizure-induced activity are largely maintained in aged rats, their RNA transcript levels are altered, which might contribute to cognitive deficits frequently observed with advancing age.