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Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils

机译：多形核中性粒细胞延时显微镜视频中非刚性对象的自动分割和跟踪

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摘要

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. (C) 2014 Elsevier B.V. All rights reserved.
机译：延时显微镜是研究各种生物过程动力学的重要技术。显微镜视频的劳动密集型手动分析越来越多地被自动分段和跟踪方法所取代。这些方法通常限于某些细胞形态和/或细胞染色。在本文中，我们提出了没有这些限制的自动分段和跟踪框架。特别是，我们的框架可以处理高度可变的细胞形状，并且不依赖于任何细胞染色。我们的分割方法基于时空图像变化的组合，以检测显微视频中的运动细胞。此方法在物体检测中产生99％的灵敏度和95％的精度。跟踪细胞包括不同的步骤，从基于最近邻居的方法进行单细胞跟踪，检测细胞之间的相互作用和分裂细胞簇，最后使用图论方法组合小径。分割和跟踪框架应用于具有不同细胞密度的合成数据集和实验数据集，这意味着不同数量的细胞-细胞相互作用。我们建立了一个验证框架来衡量我们跟踪技术的性能。发现所有数据集的细胞跟踪准确性均> 99％，这表明在不同时间点之间连接检测到的细胞具有很高的准确性。 （C）2014 Elsevier B.V.保留所有权利。

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