Publication detail

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

VIČAR, T. GUMULEC, J. BALVAN, J. HRACHO, M. KOLÁŘ, R.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

deep learning, quantitative phase imaging, cell analysis, cell nuclei segmentation

Authors

VIČAR, T.; GUMULEC, J.; BALVAN, J.; HRACHO, M.; KOLÁŘ, R.

Released

2. 1. 2019

Publisher

Springer, Singapore

ISBN

978-981-10-9034-9

Book

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

Pages from

239

Pages to

242

Pages count

4

URL

https://link.springer.com/chapter/10.1007/978-981-10-9035-6_43

BibTex

@inproceedings{BUT147411,
  author="Tomáš {Vičar} and Jaromír {Gumulec} and Jan {Balvan} and Michal {Hracho} and Radim {Kolář}",
  title="Label-free nuclear staining reconstruction in quantitative phase images using deep learning",
  booktitle="World Congress on Medical Physics and Biomedical Engineering, June 3-8, 2018, Prague, Czech Republic",
  year="2019",
  pages="239--242",
  publisher="Springer, Singapore",
  doi="10.1007/978-981-10-9035-6\{_}43",
  isbn="978-981-10-9034-9",
  url="https://link.springer.com/chapter/10.1007/978-981-10-9035-6_43"
}