CellProfiler 3.0: Next-generation image processing for biology

Claire McQuin(Broad Institute), Allen Goodman(Broad Institute), Vasiliy S. Chernyshev(Skolkovo Institute of Science and Technology), Lee Kamentsky(Broad Institute), Beth A. Cimini(Broad Institute), Kyle W. Karhohs(Broad Institute), Minh Doan(Broad Institute), Liya Ding(Allen Institute for Cell Science), Susanne M. Rafelski(Allen Institute for Cell Science), Derek Thirstrup(Allen Institute for Cell Science), Winfried Wiegraebe(Allen Institute for Cell Science), Shantanu Singh(Broad Institute), Tim Becker(Broad Institute), Juan Carlos Caicedo(Broad Institute), Anne E. Carpenter(Broad Institute)
PLoS Biology
July 3, 2018
10.1371/journal.pbio.2005970
Cited by 2,122Open Access
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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.

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