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

Mark-Anthony Bray; Anne E Carpenter

doi:10.1007/978-1-4939-7357-6_7

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

Methods Mol Biol. 2018:1683:89-112. doi: 10.1007/978-1-4939-7357-6_7.

Authors

Mark-Anthony Bray^{1

2}, Anne E Carpenter³

Affiliations

¹ Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
² Imaging Platform, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA.
³ Imaging Platform, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA. anne@broadinstitute.org.

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.

Keywords: Cell-based assays; High-content screening; Image analysis; Machine learning; Microscopy; Open-source software; Quality control.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Cells, Cultured
High-Throughput Screening Assays*
Image Processing, Computer-Assisted
Machine Learning*
Microscopy
Molecular Imaging* / methods
Molecular Imaging* / standards
Quality Control*
Software

Grants and funding

R01 GM089652/GM/NIGMS NIH HHS/United States