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

摘要

Abstract Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and genefunction studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and timeconsuming. 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 wholeslide 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 theCNN algorithm against previously published data in rats and mice, where tyrosine hydroxylase (TH )immunoreactive neurons were counted using unbiased stereology. The developedCNN algorithm and fully cloudembedded Aiforia" platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.