A deep convolutional neural network approach for astrocyte detection.

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.

• Authors: Ilida Suleymanova, Tamas Balassa, Sushil Tripathi, Csaba Molnar, Mart Saarma, Yulia Sidorova, Peter Horvath
• Journal: Scientific reports (Sci Rep) Vol. 8 Issue 1 Pg. 12878 (08 27 2018) ISSN: 2045-2322 [Electronic] England
• PMID: 30150631 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Animals
• Area Under Curve
• Astrocytes (physiology)
• Humans
• Image Processing, Computer-Assisted
• Machine Learning
• Models, Biological
• Neural Networks, Computer
• Rats
• Software