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

Result code in 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>
Alternative codes found
RIV/00216224:14110/19:00108228
Result on the web
<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>

Result language
angličtina
Original language name
Label-free nuclear staining reconstruction in quantitative phase images using deep learning
Original language description
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.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA18-24089S" target="_blank" >GA18-24089S: Quantitative phase microscopy for 3D qualitative characterization of cancer cells</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
World Congress on Medical Physics and Biomedical Engineering, June 3-8, 2018, Prague, Czech Republic
ISBN
978-981-10-9034-9
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
239-242
Publisher name
Springer, Singapore
Place of publication
neuveden
Event location
Prague
Event date
Jun 3, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000450908300043

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