Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.

Methods Mol Biol

Authors	Mark-Anthony Bray Anne Carpenter
Keywords	Software Cells, Cultured Molecular Imaging Machine Learning High-Throughput Screening Assays Image Processing, Computer-Assisted Quality Control Microscopy
Abstract	Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus images, debris, and auto-fluorescing samples can cause artifacts such as focus blur and image saturation, contaminating downstream analysis and impairing identification of subtle phenotypes. Here, we describe an automated quality control protocol implemented in validated open-source software, leveraging the suite of image-based measurements generated by CellProfiler and the machine-learning functionality of CellProfiler Analyst.
Year of Publication	2018
Journal	Methods Mol Biol
Volume	1683
Pages	89-112
Date Published	2018
ISSN	1940-6029
DOI	10.1007/978-1-4939-7357-6_7
PubMed ID	29082489
PubMed Central ID	PMC6112602
Links
Grant list	R01 GM089652 / GM / NIGMS NIH HHS / United States