Detail of 140911_DiR_x27_worm3



Project
SSBD:Repository
Title
3D single-channel laser-scanning confocal microscopy (LSCM) images about neuronal nuclei in C. elegans (strain JN2100) adult
Description
NA
Release, Updated
2016-05-20,
2018-11-15
License
CC BY-NC-SA
Kind
Image data based on Experiment related Quantitative data ; 140911_DiR_x27_worm3
File Formats
Data size
266.2 MB

Organism
C. elegans ( NCBITaxon:6239 )
Strain(s)
-
Cell Line
-

Datatype
neuronal nuclear dynamics
Molecular Function (MF)
Biological Process (BP)
-
Cellular Component (CC)
nucleus ( GO:0005634 )
Biological Imaging Method
XYZ Scale
XY: 0.24 micrometer/pixel, Z: 0.252 micrometer/slice
T scale
0 second for each 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 Toyoshima et al. (2016) PLoS Computational Biology, 12(6):e1004970
Related paper(s)

Yu Toyoshima, Terumasa Tokunaga, Osamu Hirose, Manami Kanamori, Takayuki Teramoto, Moon Sun Jang, Sayuri Kuge, Takeshi Ishihara, Ryo Yoshida, Yuichi Iino (2016) Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space., PLoS computational biology, Volume 12, Number 6, pp. e1004970

Published in 2016 Jun (Electronic publication in June 6, 2016, midnight )

(Abstract) To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured.
(MeSH Terms)

Contact
Yu Toyoshima , The University of Tokyo , Department of Biological Sciences , Iino Laboratory
Contributors
Yu Toyoshima, Terumasa Tokunaga, Osamu Hirose, Manami Kanamori, Takayuki Teramoto, Moon Sun Jang, Sayuri Kuge, Takeshi Ishihara, Ryo Yoshida, Yuichi Iino

OMERO Dataset
OMERO Project
External Link
Source