Summary: 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 impair 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 gener
Citation:Mark-Anthony Bray and Anne E Carpenter, “Quality control for high-throughput imaging experiments using machine learning in CellProfiler,” Methods in Molecular Biology, in press.