Workflow and metrics for image quality control in large-scale high-content screens.

Workflow and metrics for image quality control in large-scale high-content screens.
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DOI:
10.1177/1087057111420292
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发表时间:
2012-02
影响因子:
--
通讯作者:
Carpenter AE
Carpenter AE
中科院分区:
化学3区
文献类型:
--
作者:
Bray MA;Fraser AN;Hasaka TP;Carpenter AE

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自动化显微镜已经能够以排除目视检查的速度前所未有地收集图像。需要自动图像分析来识别感兴趣的样品并提取定量信息用于高含量筛选(HCS)。然而,研究人员受到缺乏指标和软件工具的阻碍,无法识别污染数据的基于图像的畸变,从而限制了实验的质量。我们已经开发和验证的方法,以确定这些图像采集伪影,防止从高通量显微镜实验的知识的最佳提取。我们已经实现了这些作为一个通用的,开源的算法和度量工具箱,生物学家可以随时使用,以提高各种生物实验的数据质量。
Automated microscopes have enabled the unprecedented collection of images at a rate that precludes visual inspection. Automated image analysis is required to identify interesting samples and extract quantitative information for high content screening (HCS). However, researchers are impeded by the lack of metrics and software tools to identify image-based aberrations that pollute data, limiting an experiment's quality. We have developed and validated approaches to identify those image acquisition artifacts that prevent optimal extraction of knowledge from high-throughput microscopy experiments. We have implemented these as a versatile, open-source toolbox of algorithms and metrics readily usable by biologists to improve data quality in a wide variety of biological experiments.
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