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

Show full item record



Permalink

http://hdl.handle.net/10138/252454

Citation

Penttinen , A-M , Parkkinen , I , Blom , S , Kopra , J , Andressoo , J-O , Pitkänen , K , Voutilainen , M H , Saarma , M & Airavaara , M 2018 , ' Implementation of deep neural networks to count dopamine neurons in substantia nigra ' , European Journal of Neuroscience , vol. 48 , no. 6 , pp. 2354-2361 . https://doi.org/10.1111/ejn.14129

Title: Implementation of deep neural networks to count dopamine neurons in substantia nigra
Author: Penttinen, Anna-Maija; Parkkinen, Ilmari; Blom, Sami; Kopra, Jaakko; Andressoo, Jaan-Olle; Pitkänen, Kari; Voutilainen, Merja H.; Saarma, Mart; Airavaara, Mikko
Contributor: University of Helsinki, Institute of Biotechnology
University of Helsinki, Institute of Biotechnology
University of Helsinki, Faculty of Pharmacy
University of Helsinki, Institute of Biotechnology
University of Helsinki, Institute of Biotechnology
University of Helsinki, Mart Saarma / Principal Investigator
University of Helsinki, Institute of Biotechnology
Date: 2018-09
Language: eng
Number of pages: 8
Belongs to series: European Journal of Neuroscience
ISSN: 0953-816X
URI: http://hdl.handle.net/10138/252454
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 (TM) platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.
Subject: artificial intelligence
cloud-based analysis
digital imaging
midbrain
stereology
VENTRAL TEGMENTAL AREA
PARKINSONS-DISEASE
OPTICAL FRACTIONATOR
PARS COMPACTA
TOTAL NUMBER
CELL NUMBER
MODEL
RAT
RECOGNITION
STEREOLOGY
3112 Neurosciences
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
Penttinen_A_M_e ... ournal_of_Neuroscience.pdf 367.1Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record