Automated three-dimensional detection and counting of neuron somata.
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.
Oberlaender
Marcel
M
Max Planck Institute of Neurobiology, Group "Cortical Column in silico", Am Klopferspitz 18, Martinsried 82152, Germany. oberlaender@neuro.mpg.de
Dercksen
Vincent J
VJ
Egger
Robert
R
Gensel
Maria
M
Sakmann
Bert
B
Hege
Hans-Christian
HC
D000818:Animals; D002452:Cell Count / Q000295:instrumentation / Q000379:methods*; D005968:Glutamate Decarboxylase / Q000378:metabolism; D019044:Image Cytometry / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007150:Immunohistochemistry / Q000379:methods; D008297:Male; D018613:Microscopy, Confocal / Q000379:methods*; D009420:Nervous System / Q000166:cytology*; D009474:Neurons / Q000166:cytology* / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D017208:Rats, Wistar; D012984:Software; D013194:Staining and Labeling / Q000379:methods; D005680:gamma-Aminobutyric Acid / Q000378:metabolism
D005680:gamma-Aminobutyric Acid; D005968:Glutamate Decarboxylase; C402565:glutamate decarboxylase 1