Automated Image Analysis for High-Content Screening and Analysis
Automated Image Analysis for High-Content Screening and Analysis
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DOI：
10.1177/1087057110370894
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发表时间：
2010-08-01
影响因子：
--
通讯作者：
Murphy, Robert F.
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文献类型：
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作者：
Shariff, Aabid;Kangas, Joshua;Murphy, Robert F.
高内容筛选和分析领域包括一套使用大量图像数据进行细胞生物学和药物开发自动发现的方法。在大多数情况下，成像是通过自动显微镜进行的，通常由自动液体处理和细胞培养辅助。图像处理、计算机视觉和机器学习用于将高维图像数据自动处理成有意义的细胞生物学结果。关键是创建自动化分析管道，通常包括4个基本步骤：（1）图像处理（标准化，分割，跟踪，跟踪），（2）空间变换，将图像带到公共参考系（配准），（3）计算图像特征，以及（4）用于建模和解释数据的机器学习。这里介绍这些图像分析工具的概述，沿着一些应用的简要说明。（Journal of Biomolecular Screening 2010：726-734）
The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications. (Journal of Biomolecular Screening 2010:726-734)