Label-Free Nuclear Staining Reconstruction in Quantitative Phase Images Using Deep Learning

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Publikace nespadá pod Ekonomicko-správní fakultu, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.
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VIČAR Tomáš GUMULEC Jaromír BALVAN Jan HRACHO Michal KOLAR R.

Rok publikování 2019
Druh Článek ve sborníku
Konference WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www http://dx.doi.org/10.1007/978-981-10-9035-6_43
Doi http://dx.doi.org/10.1007/978-981-10-9035-6_43
Klíčová slova Deep learning; Quantitative phase imaging; Cell analysis; Cell nuclei segmentation
Popis 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.
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