An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy.
An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy.
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DOI：
10.1186/1471-2105-14-297
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发表时间：
2013-10-04
影响因子：
3
通讯作者：
Theis FJ
中科院分区：
文献类型：
--
作者：
Buggenthin F;Marr C;Schwarzfischer M;Hoppe PS;Hilsenbeck O;Schroeder T;Theis FJ
近年来，高通量显微镜已成为一个强大的工具，分析细胞动力学在一个前所未有的高分辨率的方式。例如，在长期延时显微镜实验中产生的数据量需要自动化方法进行处理和分析。现有的软件框架非常适合荧光图像的高通量处理，但它们通常在实验室，设置甚至单个实验之间变化很大的明场图像数据上表现不佳。在这方面的贡献，我们提出了一个完全自动化的图像处理流水线，能够鲁棒地分割和分析细胞与椭球形态从明场显微镜在高通量，但时间效率的方式。该流水线包括两个步骤：（i）调整图像采集以获得用于自动处理的最佳明场图像质量。(ii)一系列快速执行的图像处理算法稳健地识别每个图像中的单个细胞。我们将该方法应用于一部延时电影，该电影由超过6天的315，000张分化造血干细胞的图像组成。我们通过比较识别的细胞数与手动计数来评估我们的方法的准确性。我们的方法是能够分割图像与不同的细胞密度和不同的细胞类型，而无需参数调整，明显优于标准的方法。通过计算群体倍增时间，我们能够在整个电影中识别干细胞群体的三个生长阶段，并通过单细胞跟踪的细胞周期时间验证我们的结果。我们的方法允许高通量明场显微镜数据的全自动处理和分析。细胞检测的鲁棒性和快速计算时间将支持高内容筛选实验的分析，延时实验的在线分析以及自动跟踪单细胞谱系的方法的开发。
In recent years, high-throughput microscopy has emerged as a powerful tool to analyze cellular dynamics in an unprecedentedly high resolved manner. The amount of data that is generated, for example in long-term time-lapse microscopy experiments, requires automated methods for processing and analysis. Available software frameworks are well suited for high-throughput processing of fluorescence images, but they often do not perform well on bright field image data that varies considerably between laboratories, setups, and even single experiments. In this contribution, we present a fully automated image processing pipeline that is able to robustly segment and analyze cells with ellipsoid morphology from bright field microscopy in a high-throughput, yet time efficient manner. The pipeline comprises two steps: (i) Image acquisition is adjusted to obtain optimal bright field image quality for automatic processing. (ii) A concatenation of fast performing image processing algorithms robustly identifies single cells in each image. We applied the method to a time-lapse movie consisting of ∼315,000 images of differentiating hematopoietic stem cells over 6 days. We evaluated the accuracy of our method by comparing the number of identified cells with manual counts. Our method is able to segment images with varying cell density and different cell types without parameter adjustment and clearly outperforms a standard approach. By computing population doubling times, we were able to identify three growth phases in the stem cell population throughout the whole movie, and validated our result with cell cycle times from single cell tracking. Our method allows fully automated processing and analysis of high-throughput bright field microscopy data. The robustness of cell detection and fast computation time will support the analysis of high-content screening experiments, on-line analysis of time-lapse experiments as well as development of methods to automatically track single-cell genealogies.
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影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
3
作者：
Knapp B;Rebhan I;Kumar A;Matula P;Kiani NA;Binder M;Erfle H;Rohr K;Eils R;Bartenschlager R;Kaderali L
通讯作者：
Kaderali L
影响因子：
64.5
作者：
Kiel, MJ;Yilmaz, ÖH;Morrison, SJ
通讯作者：
Morrison, SJ
影响因子：
10.6
作者：
Li, Chunming;Xu, Chenyang;Fox, Martin D.
通讯作者：
Fox, Martin D.
影响因子：
8
作者：
Bai, Xiangzhi;Sun, Changming;Zhou, Fugen
通讯作者：
Zhou, Fugen