Aiforia speeds up research on Parkinson’s disease
When studying Parkinson’s disease, it is essential to recognize and count the neuronal cells from microscopic brain sections. Stereology is the current gold standard to count the neurons, but time-consuming and prone to human errors, since it requires a lot of manual hands-on work.
Watch how Aiforia AI is used in University of Helsinki study to count Dopamine neurons in substantia nigra.
Eur J Neurosci. 2018;1–8.
Implementation of deep neural networks to count dopamine neurons in substantia nigra
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 highcapacity 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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