A deep convolutional neural network approach for astrocyte detection

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Suleymanova , I , Balassa , T , Tripathi , S , Molnar , C , Saarma , M , Sidorova , Y & Horvath , P 2018 , ' A deep convolutional neural network approach for astrocyte detection ' , Scientific Reports , vol. 8 , 12878 . https://doi.org/10.1038/s41598-018-31284-x

Title: A deep convolutional neural network approach for astrocyte detection
Author: Suleymanova, Ilida; Balassa, Tamas; Tripathi, Sushil; Molnar, Csaba; Saarma, Mart; Sidorova, Yulia; Horvath, Peter
Contributor: University of Helsinki, Institute of Biotechnology
University of Helsinki, Research Programs Unit
University of Helsinki, Doctoral Programme in Integrative Life Science
University of Helsinki, Institute of Biotechnology
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2018-08-27
Language: eng
Number of pages: 7
Belongs to series: Scientific Reports
ISSN: 2045-2322
URI: http://hdl.handle.net/10138/243765
Abstract: Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images and elucidating the temporal characteristics of morphological and biochemical changes is essential to understand the role of astrocytes in physiological and pathological conditions. Nowadays, manual stereological cell counting or semi-automatic segmentation techniques are widely used for the quantitative analysis of microscopy images. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. We have developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides precision comparable to those of human experts. Additionally, the runtime of cell detection is significantly less than that of other three computational methods analysed, and it is faster than human observers by orders of magnitude. We applied our DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT), as morphine tolerance is believed to activate glia. We have demonstrated a strong positive correlation between manual and DCNN-based quantification of astrocytes in rat brain.
Subject: MORPHINE-TOLERANCE
CELL DETECTION
IMAGES
PAIN
MICROSCOPY
ACTIVATION
3112 Neurosciences
3111 Biomedicine
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