CellProfiler 3.0: Next-generation image processing for biology.

PLoS Biol

Authors	Claire McQuin Allen Goodman Vasiliy Chernyshev Lee Kamentsky Beth Cimini Kyle Karhohs Minh Doan Liya Ding Susanne Rafelski Derek Thirstrup Winfried Wiegraebe Shantanu Singh Tim Becker Juan Caicedo Anne Carpenter
Keywords	Animals Humans Mice DNA Software Cell Nucleus Induced Pluripotent Stem Cells RNA, Messenger Image Processing, Computer-Assisted Imaging, Three-Dimensional Deep Learning
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.
Year of Publication	2018
Journal	PLoS Biol
Volume	16
Issue	7
Pages	e2005970
Date Published	2018 07
ISSN	1545-7885
DOI	10.1371/journal.pbio.2005970
PubMed ID	29969450
PubMed Central ID	PMC6029841
Links
Grant list	R35 GM122547 / GM / NIGMS NIH HHS / United States