Label-free nuclear staining reconstruction in quantitative phase images using deep learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU127913" target="_blank" >RIV/00216305:26220/18:PU127913 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/19:00108228

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-981-10-9035-6_43" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-10-9035-6_43</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-10-9035-6_43" target="_blank" >10.1007/978-981-10-9035-6_43</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Label-free nuclear staining reconstruction in quantitative phase images using deep learning

Popis výsledku v původním jazyce

Fluorescence microscopy is a golden standard for contemporary biological studies. However, since fluorescent dyes cross-react with biological processes, a label-free approach is more desirable. The aim of this study is to create artificial, fluorescence-like nuclei labeling from label-free images using Convolution Neural Network (CNN), where training data are easy to obtain if simultaneous label-free and fluorescence acquisition is available. This approach was tested on holographic microscopic image set of prostate non-tumor tissue (PNT1A) and metastatic tumor tissue (DU145) cells. SegNet and U-Net were tested and provide "synthetic" fluorescence staining, which are qualitatively sufficient for further analysis. Improvement was achieved with addition of bright-field image (by-product of holographic quantitative phase imaging) into analysis and two step learning approach, without and with augmentation, were introduced. Reconstructed staining was used for nucleus segmentation where 0.784 and 0.781 dice coefficient (for DU145 and PNT1A) were achieved.

Název v anglickém jazyce

Label-free nuclear staining reconstruction in quantitative phase images using deep learning

Popis výsledku anglicky

Fluorescence microscopy is a golden standard for contemporary biological studies. However, since fluorescent dyes cross-react with biological processes, a label-free approach is more desirable. The aim of this study is to create artificial, fluorescence-like nuclei labeling from label-free images using Convolution Neural Network (CNN), where training data are easy to obtain if simultaneous label-free and fluorescence acquisition is available. This approach was tested on holographic microscopic image set of prostate non-tumor tissue (PNT1A) and metastatic tumor tissue (DU145) cells. SegNet and U-Net were tested and provide "synthetic" fluorescence staining, which are qualitatively sufficient for further analysis. Improvement was achieved with addition of bright-field image (by-product of holographic quantitative phase imaging) into analysis and two step learning approach, without and with augmentation, were introduced. Reconstructed staining was used for nucleus segmentation where 0.784 and 0.781 dice coefficient (for DU145 and PNT1A) were achieved.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/GA18-24089S" target="_blank" >GA18-24089S: Kvantitativní fázová mikroskopie pro 3D kvalitativní charakterizaci nádorových buněk</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

World Congress on Medical Physics and Biomedical Engineering, June 3-8, 2018, Prague, Czech Republic

ISBN

978-981-10-9034-9

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

239-242

Název nakladatele

Springer, Singapore

Místo vydání

neuveden

Místo konání akce

Prague

Datum konání akce

3. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000450908300043