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.
Highlights
Quantification of cell numbers is a fundamental aspect of biological research
The counting quality of the developed convolutional neural networks (CNN) algorithm was good compared to human observers, and the lesion size estimates obtained with automatic CNN analysis correlate well with the estimates obtained with stereology and unbiased counting rules
Unbiased estimates of cell number in the target structure are an important measure in comparative physiology and neuroscience, among other fields
Summary
Jane and Aatos Erkko Foundation; Tekes, the Finnish Funding Agency for Technology and Innovatoin, Grant/Award Number: 3iRegeneration; Sigrid Jusélius Foundation; Academy of Finland, Grant/Award Number: #256398 #126291 #250275 #309489; Instrumentarium Science Foundation
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