Abstract

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler's infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.

Keywords

WorkflowPlug-inBiologyModular designSoftwareComputer scienceInterface (matter)Image (mathematics)Computational biologyData scienceBioinformaticsArtificial intelligenceDatabase

MeSH Terms

AnimalsCell NucleusDNADeep LearningHumansImage ProcessingComputer-AssistedImagingThree-DimensionalInduced Pluripotent Stem CellsMiceRNAMessengerSoftware

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Publication Info

Year
2018
Type
article
Volume
16
Issue
7
Pages
e2005970-e2005970
Citations
2048
Access
Closed

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2048
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144
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Cite This

Claire McQuin, Allen Goodman, Vasiliy S. Chernyshev et al. (2018). CellProfiler 3.0: Next-generation image processing for biology. PLoS Biology , 16 (7) , e2005970-e2005970. https://doi.org/10.1371/journal.pbio.2005970

Identifiers

DOI
10.1371/journal.pbio.2005970
PMID
29969450
PMCID
PMC6029841

Data Quality

Data completeness: 90%