A computerized image analysis system for quantitative analysis of cells in histological brain sections.
We propose a reliable method for automatic counting of cells in brain sections labeled with different antibodies (against NeuN, parvalbumin, GABA and c-Fos) and in Nissl-staining. Images of stained sections are converted to binary images by thresholding. Clusters of 'ON pixels' (value of 1) corresponding to cell bodies are selected based on size. The parameters of the algorithm (intensity range and cluster-size) are adjusted for different methods of staining according to expert knowledge. The automatic cell counting method (ACCM) provides correct counting results, as demonstrated by a comparison of computational results with counts gained by human experimenters and with a commercially available image analysis system. On the basis of ACCM counts, small and perhaps physiologically relevant differences in the number of labeled cells can be revealed, as demonstrated here for the GABAergic system following electrical stimulation.
Benali
Alia
A
Institut für Neuroinformatik, Ruhr-Universität Bochum, Universitätsstr. 150, 44780, Bochum, Germany. alia.benali@ruhr-uni-bochum.de
Leefken
Iris
I
Eysel
Ulf T
UT
Weiler
Elke
E
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology*; D002452:Cell Count / Q000379:methods; D016000:Cluster Analysis; D004558:Electric Stimulation; D001330:Electronic Data Processing; D007839:Functional Laterality; D006652:Histological Techniques; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry; D009562:Nissl Bodies; D010320:Parvalbumins / Q000378:metabolism; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D012984:Software; D013194:Staining and Labeling; D005680:gamma-Aminobutyric Acid / Q000378:metabolism
D010320:Parvalbumins; D005680:gamma-Aminobutyric Acid