A deep convolutional neural network approach for astrocyte detection

Show full item record




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 organization: Institute of Biotechnology
Helsinki Institute of Life Science HiLIFE
University of Helsinki
Research Programs Unit
Faculty of Medicine
Doctoral Programme in Integrative Life Science
Doctoral Programme in Drug Research
Doctoral Programme Brain & Mind
Mart Saarma / Principal Investigator
Institute for Molecular Medicine Finland
Date: 2018-08-27
Language: eng
Number of pages: 7
Belongs to series: Scientific Reports
ISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-018-31284-x
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.
3112 Neurosciences
3111 Biomedicine
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

Files in this item

Total number of downloads: Loading...

Files Size Format View
s41598_018_31284_x.pdf 1.353Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record