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

The result's identifiers

  • 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>

Alternative languages

  • 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

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20601 - Medical engineering

Result continuities

  • 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)

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • 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