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J Neurosci Methods. 2011 Mar 15;196(1):12-9. doi: 10.1016/j.jneumeth.2010.12.007. Epub 2010 Dec 29.

Semi-automated atlas-based analysis of brain histological sections.

Author information

1
HHMI/Princeton University, Lewis Thomas Lab, Department of Molecular Biology, Princeton, NJ 08544, United States. ckopec@princeton.edu

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.

PMID:
21194546
PMCID:
PMC3075115
DOI:
10.1016/j.jneumeth.2010.12.007
[Indexed for MEDLINE]
Free PMC Article

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