Detail of Fig3_Dataset1b_nuclei_image

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Project
SSBD:Repository
Title
Time-lapse images of tracking cell nucleis during movements in dataset worm 1b of C. elegans
Description
Time-lapse images of tracking cell nucleis during movements in dataset worm 1b of C. elegans
Release, Updated
2022-03-31
License
CC-BY
Kind
Image data
File Formats
uncompressed TIFF
Data size
2.5 GB

Organism
Caenorhabditis elegans ( NCBITaxon:6239 )
Strain(s)
KDK54165
Cell Line
-

Datatype
-
Molecular Function (MF)
-
Biological Process (BP)
nucleus localization ( GO:0051647 )
Cellular Component (CC)
nucleus ( GO:0005634 )
Biological Imaging Method
time lapse microscopy ( Fbbi:00000249 )
X scale
0.16 micrometer/pixel
Y scale
0.16 micrometer/pixel
Z scale
1.5 micrometer/slice
T scale
1.015 sec per 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 Wen C, et. al. (2021) Elife., 2021:10:e59187.
Related paper(s)

Chentao Wen, Takuya Miura, Venkatakaushik Voleti, Kazushi Yamaguchi, Motosuke Tsutsumi, Kei Yamamoto, Kohei Otomo, Yukako Fujie, Takayuki Teramoto, Takeshi Ishihara, Kazuhiro Aoki, Tomomi Nemoto, Elizabeth Mc Hillman, Koutarou D Kimura (2021) 3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images., eLife, Volume 10

Published in 2021 Mar 30 (Electronic publication in March 30, 2021, midnight )

(Abstract) Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and 'straightened' freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90-100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.
(MeSH Terms)

Contact
Chentao Wen , Graduate School of Science, Nagoya City University
Contributors
Chentao WenChentao Wen

OMERO Dataset
OMERO Project
Source