Detail of 088044_L1

BDML file for quantitative information about nuclei centroids of mouse embryonic cells,quantitative information about nuclei centroids of mouse embryonic cells
Release, Updated
Quantitative data based on Experiment , related Image data - 088044_L1
File Formats
Data size
1.3 MB

M. musculus ( NCBITaxon:10090 )
Cell Line

nuclear positions
Molecular Function (MF)
Biological Process (BP)
embryo development ( GO:0009790 )
Cellular Component (CC)
nucleus ( GO:0005634 )
Biological Imaging Method
XYZ Scale
XY: 0.385 micrometer/pixel, Z: 0.375 micrometer/slice
T scale
10 minute 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 Bashar et al. (2012) PLoS ONE 7, e35550.
Related paper(s)

Md Khayrul Bashar, Koji Komatsu, Toshihiko Fujimori, Tetsuya J Kobayashi (2012) Automatic extraction of nuclei centroids of mouse embryonic cells from fluorescence microscopy images., PloS one, Volume 7, Number 5, pp. e35550

Published in 2012 (Electronic publication in May 8, 2012, midnight )

(Abstract) Accurate identification of cell nuclei and their tracking using three dimensional (3D) microscopic images is a demanding task in many biological studies. Manual identification of nuclei centroids from images is an error-prone task, sometimes impossible to accomplish due to low contrast and the presence of noise. Nonetheless, only a few methods are available for 3D bioimaging applications, which sharply contrast with 2D analysis, where many methods already exist. In addition, most methods essentially adopt segmentation for which a reliable solution is still unknown, especially for 3D bio-images having juxtaposed cells. In this work, we propose a new method that can directly extract nuclei centroids from fluorescence microscopy images. This method involves three steps: (i) Pre-processing, (ii) Local enhancement, and (iii) Centroid extraction. The first step includes two variations: first variation (Variant-1) uses the whole 3D pre-processed image, whereas the second one (Variant-2) modifies the preprocessed image to the candidate regions or the candidate hybrid image for further processing. At the second step, a multiscale cube filtering is employed in order to locally enhance the pre-processed image. Centroid extraction in the third step consists of three stages. In Stage-1, we compute a local characteristic ratio at every voxel and extract local maxima regions as candidate centroids using a ratio threshold. Stage-2 processing removes spurious centroids from Stage-1 results by analyzing shapes of intensity profiles from the enhanced image. An iterative procedure based on the nearest neighborhood principle is then proposed to combine if there are fragmented nuclei. Both qualitative and quantitative analyses on a set of 100 images of 3D mouse embryo are performed. Investigations reveal a promising achievement of the technique presented in terms of average sensitivity and precision (i.e., 88.04% and 91.30% for Variant-1; 86.19% and 95.00% for Variant-2), when compared with an existing method (86.06% and 90.11%), originally developed for analyzing C. elegans images.
(MeSH Terms)

Md. Khayrul Bashar , The University of Tokyo , Institute of Industrial Science , Laboratory for Quantitative Biology
Md. Khayrul Bashar, Koji Komatsu, Toshihiko Fujimori, Tetsuya J. Kobayashi

Local ID