Detail of Figure3_test_4Embryos_QCANet

Time-series 3D images of early mouse embryos with nuclei fluorescently labeled with mCherry fused to the chromatin marker histone H2B
Time-series 3D images of early mouse embryos with nuclei fluorescently labeled with mCherry fused to the chromatin marker histone H2B
Release, Updated
Image data
File Formats
uncompressed TIFF
Data size
56.9 MB

Mus musculus ( NCBITaxon:10090 )
Cell Line
Protein names
Protein tags

Molecular Function (MF)
Biological Process (BP)
embryo development ( GO:0009790 )
Cellular Component (CC)
nucleus ( GO:0005634 )
Biological Imaging Method
fluorescence microscopy ( Fbbi:00000246 )
CV1000 microscope equipped with a 20x oil lens
X scale
0.8 micrometer/pixel
Y scale
0.8 micrometer/pixel
Z scale
2 micrometer/pixel
T scale
10 minutes of time interval

Image Acquisition
Experiment type
Microscope type
Acquisition mode
Contrast method
Microscope model
Detector model
Objective model
Filter set

Summary of Methods
See details in Tokuoka Y, et. al. (2020) NPJ Syst Biol Appl, 2020 Oct 20;6(1):32.
Related paper(s)

Yuta Tokuoka, Takahiro G Yamada, Daisuke Mashiko, Zenki Ikeda, Noriko F Hiroi, Tetsuya J Kobayashi, Kazuo Yamagata, Akira Funahashi (2020) 3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis., NPJ systems biology and applications, Volume 6, Number 1, pp. 32

Published in 2020 Oct 20 (Electronic publication in Oct. 20, 2020, midnight )

(Abstract) During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To acquire quantitative criteria of embryogenesis from time-series 3D microscopic images, image processing algorithms such as segmentation have been applied. Because the cells in embryos are considerably crowded, an algorithm to segment individual cells in detail and accurately is needed. To quantify the nuclear region of every cell from a time-series 3D fluorescence microscopic image of living cells, we developed QCANet, a convolutional neural network-based segmentation algorithm for 3D fluorescence bioimages. We demonstrated that QCANet outperformed 3D Mask R-CNN, which is currently considered as the best algorithm of instance segmentation. We showed that QCANet can be applied not only to developing mouse embryos but also to developing embryos of two other model species. Using QCANet, we were able to extract several quantitative criteria of embryogenesis from 11 early mouse embryos. We showed that the extracted criteria could be used to evaluate the differences between individual embryos. This study contributes to the development of fundamental approaches for assessing embryogenesis on the basis of extracted quantitative criteria.
(MeSH Terms)

Akira Funahashi , Keio University , Department of Biosciencesand Informatics
Yuta Tokuoka

OMERO Dataset
OMERO Project