Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany; Friedrich Schiller University, Jena, Germany.
Septomics Research Center, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany; Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany.
Med Image Anal. 2015 Feb;20(1):34-51. doi: 10.1016/j.media.2014.10.002. Epub 2014 Nov 8.
Time-lapse microscopy is an important technique to study the dynamics of various biological processes. The labor-intensive manual analysis of microscopy videos is increasingly replaced by automated segmentation and tracking methods. These methods are often limited to certain cell morphologies and/or cell stainings. In this paper, we present an automated segmentation and tracking framework that does not have these restrictions. In particular, our framework handles highly variable cell shapes and does not rely on any cell stainings. Our segmentation approach is based on a combination of spatial and temporal image variations to detect moving cells in microscopy videos. This method yields a sensitivity of 99% and a precision of 95% in object detection. The tracking of cells consists of different steps, starting from single-cell tracking based on a nearest-neighbor-approach, detection of cell-cell interactions and splitting of cell clusters, and finally combining tracklets using methods from graph theory. The segmentation and tracking framework was applied to synthetic as well as experimental datasets with varying cell densities implying different numbers of cell-cell interactions. We established a validation framework to measure the performance of our tracking technique. The cell tracking accuracy was found to be >99% for all datasets indicating a high accuracy for connecting the detected cells between different time points.
延时显微镜技术是研究各种生物过程动态的重要技术。显微镜视频的人工分析工作越来越多地被自动分割和跟踪方法所取代。这些方法通常仅限于某些细胞形态和/或细胞染色。在本文中，我们提出了一种不受这些限制的自动分割和跟踪框架。特别是，我们的框架处理高度可变的细胞形状，不依赖任何细胞染色。我们的分割方法基于时空图像变化的组合，以检测显微镜视频中的移动细胞。这种方法在目标检测中达到了 99%的灵敏度和 95%的精度。细胞的跟踪包括不同的步骤，从基于最近邻方法的单细胞跟踪开始，检测细胞-细胞相互作用和细胞簇的分裂，最后使用图论方法合并轨迹。分割和跟踪框架应用于具有不同细胞密度的合成和实验数据集，这意味着细胞-细胞相互作用的数量不同。我们建立了一个验证框架来衡量我们的跟踪技术的性能。对于所有数据集，细胞跟踪的准确性都>99%，这表明在不同时间点之间连接检测到的细胞的准确性很高。
