A novel deep learning-based method for automatic stereology of microglia cells from low magnification images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11140%2F24%3A10479067" target="_blank" >RIV/00216208:11140/24:10479067 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=mxNbxQX_m3" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=mxNbxQX_m3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ntt.2024.107336" target="_blank" >10.1016/j.ntt.2024.107336</a>
Alternative languages
Result language
angličtina
Original language name
A novel deep learning-based method for automatic stereology of microglia cells from low magnification images
Original language description
Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20x) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60x greater efficiency than manual stereology (<1 min vs. TILDE OPERATOR+D91 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30502 - Other medical science
Result continuities
Project
<a href="/en/project/EF16_019%2F0000787" target="_blank" >EF16_019/0000787: Fighting INfectious Diseases</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurotoxicology and Teratology
ISSN
0892-0362
e-ISSN
1872-9738
Volume of the periodical
102
Issue of the periodical within the volume
March-April
Country of publishing house
GB - UNITED KINGDOM
Number of pages
8
Pages from-to
107336
UT code for WoS article
001205983600001
EID of the result in the Scopus database
2-s2.0-85187172891