Implementation of deep neural networks to count dopamine neurons in substantia nigra

The European Journal of Neuroscience
Anna-Maija PenttinenMikko Airavaara

Abstract

Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and time-consuming. The development of neural networks and deep learning has opened a new way to teach computers to count neurons. Implementation of a programming paradigm enables a computer to learn from the data and development of an automated cell counting method. The advantages of computerized counting are reproducibility, elimination of human error and fast high-capacity analysis. We implemented whole-slide digital imaging and deep convolutional neural networks (CNN) to count substantia nigra dopamine neurons. We compared the results of the developed method against independent manual counting by human observers and validated the CNN algorithm against previously published data in rats and mice, where tyrosine hydroxylase (TH)-immunoreactive neurons were counted using unbiased stereology. The developed CNN algorithm and fully cloud-embedded Aiforia™ platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.

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Citations

Feb 13, 2020·Current Protocols in Neuroscience·Mikko AiravaaraAndrii Domanskyi
Nov 4, 2020·Journal of Applied Toxicology : JAT·Anshul Srivastava, Joseph P Hanig
Jan 14, 2021·Scientific Reports·Luca MasinLies De Groef
May 25, 2021·Journal of Parkinson's Disease·Juho-Matti RenkoYulia A Sidorova
Feb 8, 2022·Cellular and Molecular Life Sciences : CMLS·Emmi PakarinenMaria Lindahl

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Methods Mentioned

BETA
dissector

Software Mentioned

ImageJ
StereoInvestigator
Fimmic
CellProfiler

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