Fast automatic quantitative cell replication with fluorescent live cell imaging

An 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).A 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.Live cell imaging is an useful tool to monitor cellular activities in living systems and to study complex biological processes in great detail [1]. In recent years, technological advances include sensor sensitivity, computing power, brighter and more-stable fluorescent proteins, but expertise in the automated image analysis is required to harness the full potential that live-cell microscopy offers. Kitamura et al. [2] showed that live cell imaging reveals replication of individual replicons in eukaryotic replication factories, using time-lapse microscopy. In our investigation on BRCA1 [3], p63 [4] and Scr [5] in breast cancer, a negative correlation was discovered by manual observation in live cell imaging between the cell replication activities and stain expression level using fluorescence microscopy (Figure 1). That is the higher degree of blue stain appears, the less cell replication activity occurs. (More information on the associated biological study has been published in [6].)However, manual quantification is subjective and results