Abstract
Imaging applications often produce large numbers of data sets, which need to be processed in a uniform and unbiased manner to obtain precise information about the number and size of cells or cell densities in different regions of the brain. Machine learning is a novel method here introduced to adjust algorithms to the biological requirements and to evaluate cellular features of tissue samples in an automated manner. In this chapter we describe methods to prepare mouse brain tissue for subsequent image processing and data evaluation. We give information in a step-by-step manner how to choose and perform appropriate fixation protocols, decide for suitable sectioning, and give hints what to consider when performing immunofluorescence stainings. Furthermore, we introduce the Machine Learning-Based Image Segmentation (MLBIS) to determine neuronal cell density in brain slices.
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Acknowledgements
We thank Clemens Thölken and Jens Klinzing for early work on developing a processing pipeline and establishing machine learning algorithms in our lab. We also appreciate the technical help of Prof. Günter Purschke and Werner Mangerich regarding tissue preparation. The work was supported by the Deutsche Forschungsgemeinschaft (DFG grant BR1192/11-2) to R.B. and a Lichtenberg Fellowship of the state of Lower Saxony (to F.S.).
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Penazzi, L., Sündermann, F., Bakota, L., Brandt, R. (2014). Machine Learning to Evaluate Neuron Density in Brain Sections. In: Bakota, L., Brandt, R. (eds) Laser Scanning Microscopy and Quantitative Image Analysis of Neuronal Tissue. Neuromethods, vol 87. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0381-8_13
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DOI: https://doi.org/10.1007/978-1-4939-0381-8_13
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Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-0380-1
Online ISBN: 978-1-4939-0381-8
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