title: Workflow and metrics for image quality control in large-scale high-content screens.
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.
authors.affiliation: Imaging Platform, Broad Institute of Harvard & MIT, Cambridge, MA 02142, USA. anne@broadinstitute.org
authors.forename: Mark-Anthony
authors.initials: MA
authors.lastname: Bray
authors.forename: Adam N
authors.initials: AN
authors.lastname: Fraser
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Hasaka
authors.forename: Anne E
authors.initials: AE
authors.lastname: Carpenter
mesh_terms: D000465:Algorithms; D057166:High-Throughput Screening Assays / Q000379:methods; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011786:Quality Control; D012984:Software; D057188:Workflow