Summary of 244-Tokuoka-Embryogenesis

SSBD:database URL
External Repository
Time-series 3D images and BDML file for quantiative information about early mouse embryos with labeled nuclei.
Relase date
Updated date
Image data based on Experiment
Number of Datasets
2 ( Image datasets: 2, Quantitative data datasets: 0 )
Size of Datasets
250.8 MB ( Image datasets: 250.8 MB, Quantitative data datasets: 0 bytes )

Mus musculus
Protein name(s)
Protein tag(s)
mCherry, mRFP1

Molecular Function (MF)
Biological Process (BP)
embryo development
Cellular Component (CC)
Biological Imaging Method
fluorescence microscopy, CV1000 microscope equipped with a 20x oil lens, IX71 microscope equipped with a 20x oil lens
X scale
0.8 micrometer/pixel
Y scale
0.8 micrometer/pixel
Z scale
1.75 micrometer/pixel, 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

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

Dataset List of 244-Tokuoka-Embryogenesis

Dataset ID
4D View
Download BDML
Download Images
# 8829
Dataset Kind Image data
Dataset Size 56.9 MB
4D view
Download BDML
Download Image data

# 8830
Dataset Kind Image data
Dataset Size 193.9 MB
4D view
Download BDML
Download Image data