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

J Biomol Screen
Authors
Keywords
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.

Year of Publication
2012
Journal
J Biomol Screen
Volume
17
Issue
2
Pages
266-74
Date Published
2012 Feb
ISSN
1552-454X
URL
DOI
10.1177/1087057111420292
PubMed ID
21956170
PubMed Central ID
PMC3593271
Links
Grant list
U54 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