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Workflow and Metrics for Image Quality Control in Large-scale High-content Screens

Overview
Journal J Biomol Screen
Publisher Sage Publications
Date 2011 Sep 30
PMID 21956170
Citations 47
Authors
Mark-Anthony Bray
Adam N Fraser
Thomas P Hasaka
Anne E Carpenter
Affiliations
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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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