Hilfe Wegweiser Datenschutzhinweis Impressum Kontakt





Automated three-dimensional detection and counting of neuron somata


Oberlaender,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Dercksen VJ, Egger,  R
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar

Oberlaender, M., Dercksen VJ, Egger, R., Gensel M, Sakmann, B., & Hege, H.-C. (2009). Automated three-dimensional detection and counting of neuron somata. Journal of Neuroscience Methods, 180(1), 147–160. doi:10.1016/j.jneumeth.2009.03.008.

We present a novel approach for automated detection of neuron somata. A three-step processing pipeline is described on the example of confocal image stacks of NeuN-stained neurons from rat somato-sensory cortex. It results in a set of position landmarks, representing the midpoints of all neuron somata. In the first step, foreground and background pixels are identified, resulting in a binary image. It is based on local thresholding and compensates for imaging and staining artifacts. Once this pre-processing guarantees a standard image quality, clusters of touching neurons are separated in the second step, using a marker-based watershed approach. A model-based algorithm completes the pipeline. It assumes a dominant neuron population with Gaussian distributed volumes within one microscopic field of view. Remaining larger objects are hence split or treated as a second neuron type. A variation of the processing pipeline is presented, showing that our method can also be used for co-localization of neurons in multi-channel images. As an example, we process 2-channel stacks of NeuN-stained somata, labeling all neurons, counterstained with GAD67, labeling GABAergic interneurons, using an adapted pre-processing step for the second channel. The automatically generated landmark sets are compared to manually placed counterparts. A comparison yields that the deviation in landmark position is negligible and that the difference between the numbers of manually and automatically counted neurons is less than 4. In consequence, this novel approach for neuron counting is a reliable and objective alternative to manual detection.