Fast automatic quantitative cell replication with fluorescent live cell imaging

BMC Bioinformatics

Research Article

Ching-Wei Wang¹ 

[1] Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;

关键词: Cell Replication;    Live Cell Imaging;    Cell Segmentation;    Little Square Difference;    Manual Quantification;   

DOI  :  10.1186/1471-2105-13-21

 received in 2011-07-20, accepted in 2012-01-31,  发布年份 2012

来源: Springer

【 摘 要 】

Backgroundlive cell imaging is a useful tool to monitor cellular activities in living systems. It is often necessary in cancer research or experimental research to quantify the dividing capabilities of cells or the cell proliferation level when investigating manipulations of the cells or their environment. Manual quantification of fluorescence microscopic image is difficult because human is neither sensitive to fine differences in color intensity nor effective to count and average fluorescence level among cells. However, auto-quantification is not a straightforward problem to solve. As the sampling location of the microscopy changes, the amount of cells in individual microscopic images varies, which makes simple measurement methods such as the sum of stain intensity values or the total number of positive stain within each image inapplicable. Thus, automated quantification with robust cell segmentation techniques is required.ResultsAn automated quantification system with robust cell segmentation technique are presented. The experimental results in application to monitor cellular replication activities show that the quantitative score is promising to represent the cell replication level, and scores for images from different cell replication groups are demonstrated to be statistically significantly different using ANOVA, LSD and Tukey HSD tests (p-value < 0.01). In addition, the technique is fast and takes less than 0.5 second for high resolution microscopic images (with image dimension 2560 × 1920).ConclusionA robust automated quantification method of live cell imaging is built to measure the cell replication level, providing a robust quantitative analysis system in fluorescent live cell imaging. In addition, the presented unsupervised entropy based cell segmentation for live cell images is demonstrated to be also applicable for nuclear segmentation of IHC tissue images.

【 授权许可】

CC BY   
© Wang; licensee BioMed Central Ltd. 2012