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Application and Quantitative Validation of Computer-Automated Three-Dimensional Counting of Cell Nuclei

Published online by Cambridge University Press:  31 July 2002

William Shain
Affiliation:
Wadsworth Center for Laboratories and Research, New York State Department of Health, Empire State Plaza, Box 509, Albany, NY 12201-0509 School of Public Health, The University at Albany, Albany, NY 12201-0509
Soraya Kayali
Affiliation:
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590
Donald Szarowski
Affiliation:
Wadsworth Center for Laboratories and Research, New York State Department of Health, Empire State Plaza, Box 509, Albany, NY 12201-0509
Margaret Davis-Cox
Affiliation:
School of Public Health, The University at Albany, Albany, NY 12201-0509
Hakan Ancin
Affiliation:
Department of Electrical Computing and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590
Anoop K. Bhattacharjya
Affiliation:
Department of Electrical Computing and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590
Badrinath Roysam
Affiliation:
Department of Electrical Computing and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590
James N. Turner
Affiliation:
Wadsworth Center for Laboratories and Research, New York State Department of Health, Empire State Plaza, Box 509, Albany, NY 12201-0509 School of Public Health, The University at Albany, Albany, NY 12201-0509 Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590 Department of Electrical Computing and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590
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Abstract

This study provides a quantitative validation of qualitative automated three-dimensional (3-D) analysis methods reported earlier. It demonstrates the applicability and quantitative accuracy of our method to detect, characterize, and count Feulgen stained cell nuclei in two tissues (hippocampus and testes). A laser-scanned confocal light microscope was used to record 3-D images i which our algorithms automatically identified individual nuclei from the optical sections given an estimate of minimum nuclear size. The hippocampal data sets were also manually counted independently by five trained observers using the STERECON 3-D image reconstruction system. The automated and manual counts were compared. A nucleus-by-nucleus comparison of the manual and automated counts verified that the automated analysis was accurate and reproducible, and permitted additional quantitative analyses not available from manual methods. The algorithms also identified subpopulations of nuclei within the hippocampal samples, and haploid and diploid nuclei in the testes. Our methods were shown to be repeatable, accurate, and more consistent than manual counting.

Type
Articles
Copyright
© 1999 Microscopy Society of America

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