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CellProfiler 3.0: Next-generation image processing for biology

PLoS Biology · 2018 · Vol. 16(7) · pp. e2005970–e2005970
Claire McQuinAllen GoodmanVasiliy S. ChernyshevLee KamentskyBeth A. CiminiKyle W. KarhohsMinh DoanLiya DingSusanne M. RafelskiDerek ThirstrupWinfried WiegraebeShantanu SinghTim BeckerJuan Carlos CaicedoAnne E. Carpenter

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.

Cell Image Analysis TechniquesSingle-cell and spatial transcriptomicsGenetics, Bioinformatics, and Biomedical ResearchWorkflowPlug-inBiologyModular designSoftwareComputer scienceInterface (matter)Image (mathematics)Computational biologyData science

MeSH terms

Deep LearningAnimalsCell NucleusDNAHumansImage Processing, Computer-AssistedRNA, MessengerSoftwareImaging, Three-DimensionalMiceInduced Pluripotent Stem Cells

Funding

  • Deutsche Forschungsgemeinschaft
  • Masarykova Univerzita
  • National Institutes of Health
  • Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture
Citations
2,102
FWCI
242.63
field-weighted impact
References
41
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100%
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