Department of Information Science, School of Mathematical Sciences, and LMAM, Peking University, Beijing 100871, China.
IEEE Trans Med Imaging. 2010 Jan;29(1):96-105. doi: 10.1109/TMI.2009.2027813. Epub 2009 Jul 28.
Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cell-cycle analysis software package, DCELLIQ, which is freely available.
自动细胞分割和跟踪对于使用延时荧光显微镜进行细胞周期行为的定量分析至关重要。然而，复杂、动态的细胞周期行为给现有的图像分割和跟踪方法带来了新的挑战。本文提出了一种用于定量细胞周期分析的全自动跟踪方法。在提出的跟踪方法中，我们引入了邻接图来描述相邻细胞核的空间分布，并且基于空间分布、核形态外观、迁移和强度信息设计了一种新的不相似性度量。然后，我们采用整数规划和划分匹配策略，结合新的不相似性度量来跟踪细胞核。我们将这种新的跟踪方法应用于对 HeLa 癌细胞进行多个细胞周期的跟踪，验证结果表明，分割和跟踪的准确率分别高达 99.5%和 90.0%。该跟踪方法已在免费的细胞周期分析软件包 DCELLIQ 中实现。
