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J Biomol Screen. 2012 Feb;17(2):266-74. doi: 10.1177/1087057111420292. Epub 2011 Sep 28.

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

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  • 1Imaging Platform, Broad Institute of Harvard & MIT, Cambridge, MA 02142, USA. anne@broadinstitute.org

Abstract

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 experiment quality. The authors have developed and validated approaches to identify those image acquisition artifacts that prevent optimal extraction of knowledge from high-content microscopy experiments. They 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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