A deep convolutional neural network approach for astrocyte detection

Scientific Reports
Ilida SuleymanovaPeter Horvath

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 ...Continue Reading

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Citations

Jul 23, 2020·Molecular Biology of the Cell·Réka HollandiPéter Horváth
Sep 27, 2019·Scientific Reports·Asim IqbalTheofanis Karayannis
Aug 28, 2020·The Journal of Cell Biology·Dominic WaitheChristian Eggeling
Nov 4, 2020·Journal of Applied Toxicology : JAT·Anshul Srivastava, Joseph P Hanig
Jan 14, 2021·Scientific Reports·Luca MasinLies De Groef
Jan 8, 2021·The Journal of Comparative Neurology·Jessica BlackburnJosé Javier Otero
Mar 18, 2021·Scientific Reports·Mighten C YipCraig R Forest

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Software Mentioned

Training Image Labeler ( TIL )
Pannoramic Viewer
ilastik
PyQt5
Matlab
ImageJ
caffe
ilastik script
TIL
FindMyCells

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