期刊
EUROPEAN JOURNAL OF NEUROSCIENCE
卷 48, 期 6, 页码 2354-2361出版社
WILEY
DOI: 10.1111/ejn.14129
关键词
artificial intelligence; cloud-based analysis; digital imaging; midbrain; stereology
资金
- Jane and Aatos Erkko Foundation
- Tekes, the Finnish Funding Agency for Technology and Innovatoin [3iRegeneration]
- Sigrid Juselius Foundation
- Academy of Finland [256398, 126291, 250275, 309489]
- Instrumentarium Science Foundation
- Academy of Finland (AKA) [309489, 126291, 309489, 256398, 126291, 256398] Funding Source: Academy of Finland (AKA)
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 (TM) platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.
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