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J Biomol Screen DOI:10.1177/1087057111420292

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

Publication TypeJournal Article
Year of Publication2012
AuthorsBray, M-A, Fraser, AN, Hasaka, TP, Carpenter, AE
JournalJ Biomol Screen
Date Published2012 Feb
KeywordsAlgorithms, High-Throughput Screening Assays, Image Enhancement, Image Processing, Computer-Assisted, Microscopy, Pattern Recognition, Automated, Quality Control, Software, Workflow

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.


Alternate JournalJ Biomol Screen
PubMed ID21956170
PubMed Central IDPMC3593271
Grant ListU54 HG005032 / HG / NHGRI NIH HHS / United States
R01 GM089652 / GM / NIGMS NIH HHS / United States
UL1 RR024924 / RR / NCRR NIH HHS / United States
RL1 GM084437 / GM / NIGMS NIH HHS / United States
RL1 HG004671 / HG / NHGRI NIH HHS / United States
RL1 CA133834 / CA / NCI NIH HHS / United States