Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
BMC Bioinformatics. 2012 Jan 31;13:21. doi: 10.1186/1471-2105-13-21.
live 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.
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.
活细胞成像技术是监测活系统中细胞活动的有用工具。在癌症研究或实验研究中，经常需要量化细胞的分裂能力或细胞增殖水平，例如在研究细胞或其环境的操作时。由于人类对颜色强度的细微差异既不敏感，也无法有效地对细胞间的荧光强度进行计数和平均，因此手动量化荧光显微镜图像是很困难的。然而，自动量化并不是一个容易解决的问题。由于显微镜的采样位置发生变化，每个显微镜图像中的细胞数量也会发生变化，这使得简单的测量方法（如染色强度值的总和或每个图像中阳性染色的总数）不再适用。因此，需要具有强大细胞分割技术的自动化量化方法。
提出了一种具有强大细胞分割技术的自动化量化系统。在应用于监测细胞复制活动的实验结果中，定量评分有望代表细胞复制水平，并且使用 ANOVA、LSD 和 Tukey HSD 检验（p 值<0.01）证明了来自不同细胞复制组的图像的评分在统计学上存在显著差异。此外，该技术速度快，对于高分辨率显微镜图像（图像尺寸为 2560×1920），其处理时间不到 0.5 秒。
构建了一种用于测量细胞复制水平的强大活细胞成像自动量化方法，为荧光活细胞成像提供了强大的定量分析系统。此外，所提出的用于活细胞图像的无监督熵基细胞分割方法也被证明可应用于 IHC 组织图像的核分割。
