title: Automated image analysis for high-content screening and analysis.
abstract: The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.
authors.affiliation: Lane Center for Computational Biology and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA.
authors.forename: Aabid
authors.initials: A
authors.lastname: Shariff
authors.forename: Joshua
authors.initials: J
authors.lastname: Kangas
authors.forename: Luis Pedro
authors.initials: LP
authors.lastname: Coelho
authors.forename: Shannon
authors.initials: S
authors.lastname: Quinn
authors.forename: Robert F
authors.initials: RF
authors.lastname: Murphy
mesh_terms: D000818:Animals; D001331:Automation; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007858:Learning; D013223:Statistics as Topic