Imperial College London
Alternate

ProfessorRichardNicholas

Faculty of Medicine, Department of Brain Sciences

Professor of Practice (Neurology)
 
 
 
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Contact

 

r.nicholas

 
 
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Location

 

12L12CLab BlockCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Choi:2022:10.1038/s41598-022-05815-6,
author = {Choi, S and Hill, D and Guo, L and Nicholas, R and Papadopoulos, D and Cordeiro, MF},
doi = {10.1038/s41598-022-05815-6},
journal = {Scientific Reports},
title = {Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms},
url = {http://dx.doi.org/10.1038/s41598-022-05815-6},
volume = {12},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The resident macrophages of the central nervous system, microglia, are becoming increasingly implicated as active participants in neuropathology and ageing. Their diverse and changeable morphology is tightly linked with functions they perform, enabling assessment of their activity through image analysis. To better understand the contributions of microglia in health, senescence, and disease, it is necessary to measure morphology with both speed and reliability. A machine learning approach was developed to facilitate automatic classification of images of retinal microglial cells as one of five morphotypes, using a support vector machine (SVM). The area under the receiver operating characteristic curve for this SVM was between 0.99 and 1, indicating strong performance. The densities of the different microglial morphologies were automatically assessed (using the SVM) within wholemount retinal images. Retinas used in the study were sourced from 28 healthy C57/BL6 mice split over three age points (2, 6, and 28-months). The prevalence of ‘activated’ microglial morphology was significantly higher at 6- and 28-months compared to 2-months (p < .05 and p < .01 respectively), and ‘rod’ significantly higher at 6-months than 28-months (p < 0.01). The results of the present study propose a robust cell classification SVM, and further evidence of the dynamic role microglia play in ageing.
AU  - Choi,S
AU  - Hill,D
AU  - Guo,L
AU  - Nicholas,R
AU  - Papadopoulos,D
AU  - Cordeiro,MF
DO  - 10.1038/s41598-022-05815-6
PY  - 2022///
SN  - 2045-2322
TI  - Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-022-05815-6
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000750981100096&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.nature.com/articles/s41598-022-05815-6
UR  - http://hdl.handle.net/10044/1/98129
VL  - 12
ER  -
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