Classification of Global Microglia Proliferation Based on Deep Learning with Local Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11140%2F22%3A10444957" target="_blank" >RIV/00216208:11140/22:10444957 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1117/12.2611581" target="_blank" >http://dx.doi.org/10.1117/12.2611581</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2611581" target="_blank" >10.1117/12.2611581</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Global Microglia Proliferation Based on Deep Learning with Local Images

Popis výsledku v původním jazyce

Microglial cell proliferation in neural tissue (neuroinflammation) occurs during infections, neurological disease, neurotoxicity, and other conditions. In basic science and clinical studies, quantification of microglial proliferation requires extensive manual counting (cell clicking) by trained experts (~ 2 hours per case). Previous efforts to automate this process have focused on stereology-based estimation of global cell number using deep learning (DL)based segmentation of immunostained microglial cells at high magnification. To further improve on throughput efficiency, we propose a novel approach using snapshot ensembles of convolutional neural networks (CNN) with training using local images, i.e., low (20x) magnification, to predict high or low microglial proliferation at the global level. An expert uses stereology to quantify the global microglia cell number at high magnification, applies a label of high or low proliferation at the animal (mouse) level, then assigns this global label to each 20x image as ground truth for training a CNN to predict global proliferation. To test accuracy, cross validation with six mouse brains from each class for training and one each for testing was done. The ensemble predictions were averaged, and the test brain was assigned a label based on the predicted class of the majority of images from that brain. The ensemble accurately classified proliferation in 11 of 14 brains (~ 80%) in less than a minute per case, without cell-level segmentation or manual stereology at high magnification. This approach shows, for the first time, that training a DL model with local images can efficiently predict microglial cell proliferation at the global level. The dataset used in this work is publicly available at: tinyurl.com/20xData-USF-SRC.

Název v anglickém jazyce

Classification of Global Microglia Proliferation Based on Deep Learning with Local Images

Popis výsledku anglicky

Microglial cell proliferation in neural tissue (neuroinflammation) occurs during infections, neurological disease, neurotoxicity, and other conditions. In basic science and clinical studies, quantification of microglial proliferation requires extensive manual counting (cell clicking) by trained experts (~ 2 hours per case). Previous efforts to automate this process have focused on stereology-based estimation of global cell number using deep learning (DL)based segmentation of immunostained microglial cells at high magnification. To further improve on throughput efficiency, we propose a novel approach using snapshot ensembles of convolutional neural networks (CNN) with training using local images, i.e., low (20x) magnification, to predict high or low microglial proliferation at the global level. An expert uses stereology to quantify the global microglia cell number at high magnification, applies a label of high or low proliferation at the animal (mouse) level, then assigns this global label to each 20x image as ground truth for training a CNN to predict global proliferation. To test accuracy, cross validation with six mouse brains from each class for training and one each for testing was done. The ensemble predictions were averaged, and the test brain was assigned a label based on the predicted class of the majority of images from that brain. The ensemble accurately classified proliferation in 11 of 14 brains (~ 80%) in less than a minute per case, without cell-level segmentation or manual stereology at high magnification. This approach shows, for the first time, that training a DL model with local images can efficiently predict microglial cell proliferation at the global level. The dataset used in this work is publicly available at: tinyurl.com/20xData-USF-SRC.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30502 - Other medical science

Projekt

<a href="/cs/project/EF16_019%2F0000787" target="_blank" >EF16_019/0000787: Centrum výzkumu infekčních onemocnění</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

ISBN

978-1-5106-4939-2

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

Society of Photo-Optical Instrumentation Engineers

Místo vydání

Washington

Místo konání akce

San Diego

Datum konání akce

20. 2. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000836295600085