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@inproceedings{1641740,
author = {Vičar, Tomáš and Gumulec, Jaromír and Balvan, Jan and Hracho, Michal and Kolar, R.},
address = {NEW YORK},
booktitle = {WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1},
doi = {http://dx.doi.org/10.1007/978-981-10-9035-6_43},
editor = {Lhotska, L Sukupova, L Lackovic, I Ibbott, GS},
keywords = {Deep learning; Quantitative phase imaging; Cell analysis; Cell nuclei segmentation},
howpublished = {elektronická verze "online"},
language = {eng},
location = {NEW YORK},
isbn = {978-981-10-9034-9},
pages = {239-242},
publisher = {SPRINGER},
title = {Label-Free Nuclear Staining Reconstruction in Quantitative Phase Images Using Deep Learning},
url = {http://dx.doi.org/10.1007/978-981-10-9035-6_43},
year = {2019}
}
TY - JOUR
ID - 1641740
AU - Vičar, Tomáš - Gumulec, Jaromír - Balvan, Jan - Hracho, Michal - Kolar, R.
PY - 2019
TI - Label-Free Nuclear Staining Reconstruction in Quantitative Phase Images Using Deep Learning
PB - SPRINGER
CY - NEW YORK
SN - 9789811090349
KW - Deep learning
KW - Quantitative phase imaging
KW - Cell analysis
KW - Cell nuclei segmentation
UR - http://dx.doi.org/10.1007/978-981-10-9035-6_43
L2 - http://dx.doi.org/10.1007/978-981-10-9035-6_43
N2 - 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.
ER -
VIČAR, Tomáš, Jaromír GUMULEC, Jan BALVAN, Michal HRACHO a R. KOLAR. Label-Free Nuclear Staining Reconstruction in Quantitative Phase Images Using Deep Learning. Online. In Lhotska, L Sukupova, L Lackovic, I Ibbott, GS. \textit{WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1}. NEW YORK: SPRINGER, 2019, s.~239-242. ISBN~978-981-10-9034-9. Dostupné z: https://dx.doi.org/10.1007/978-981-10-9035-6\_{}43.
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