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Methods Mol Biol DOI:10.1007/978-1-4939-7357-6_7

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

Publication TypeJournal Article
Year of Publication2018
AuthorsBray, M-A, Carpenter, AE
JournalMethods Mol Biol
Volume1683
Pages89-112
Date Published2018
ISSN1940-6029
KeywordsCells, Cultured, High-Throughput Screening Assays, Image Processing, Computer-Assisted, Machine Learning, Microscopy, Molecular Imaging, Quality Control, Software
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.

DOI10.1007/978-1-4939-7357-6_7
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/29082489?dopt=Abstract

Alternate JournalMethods Mol. Biol.
PubMed ID29082489
PubMed Central IDPMC6112602
Grant ListR01 GM089652 / GM / NIGMS NIH HHS / United States