title: Semi-automated atlas-based analysis of brain histological sections.
abstract: Quantifying the location and/or number of features in a histological section of the brain currently requires one to first, manually register a corresponding section from a tissue atlas onto the experimental section and second, count the features. No automated method exists for the first process (registering), and most automated methods for the second process (feature counting) operate reliably only in a high signal-to-noise regime. To reduce experimenter bias and inconsistencies and increase the speed of these analyses, we developed Atlas Fitter, a semi-automated, open-source MatLab-based software package that assists in rapidly registering atlas panels onto histological sections. We also developed CellCounter, a novel fully automated cell counting algorithm that is designed to operate on images with non-uniform background intensities and low signal-to-noise ratios.
authors.affiliation: HHMI/Princeton University, Lewis Thomas Lab, Department of Molecular Biology, Princeton, NJ 08544, United States. ckopec@princeton.edu
authors.forename: Charles D
authors.initials: CD
authors.lastname: Kopec
authors.forename: Amanda C
authors.initials: AC
authors.lastname: Bowers
authors.forename: Shraddha
authors.initials: S
authors.lastname: Pai
authors.forename: Carlos D
authors.initials: CD
authors.lastname: Brody
mesh_terms: D000465:Algorithms; D000818:Animals; D057205:Automation, Laboratory / Q000379:methods*; D001921:Brain / Q000033:anatomy & histology* / Q000378:metabolism; D001931:Brain Mapping; D002452:Cell Count / Q000379:methods; D003598:Cytoskeletal Proteins / Q000378:metabolism; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D009419:Nerve Tissue Proteins / Q000378:metabolism; D009474:Neurons / Q000166:cytology / Q000378:metabolism; D051381:Rats; D020318:Rats, Long-Evans
chemical_list: D003598:Cytoskeletal Proteins; D009419:Nerve Tissue Proteins; C092069:activity regulated cytoskeletal-associated protein