CellProfiler 3.0: Next-generation image processing for biology

Claire McQuin, Allen Goodman, Vasiliy Chernyshev, Lee Kamentsky, Beth A Cimini, Kyle W Karhohs, Minh Doan, Liya Ding, Susanne M Rafelski, Derek Thirstrup, Winfried Wiegraebe, Shantanu Singh, Tim Becker, Juan C Caicedo and Anne E Carpenter

PLOS Biology, 2018, vol. 16, issue 7, 1-17

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.Author summary: The “big-data revolution” has struck biology: it is now common for robots to prepare cell samples and take thousands of microscopy images. Looking at the resulting images by eye would be extremely tedious, not to mention subjective. Thus, many biologists find they need software to analyze images easily and accurately. The third major release of our free open-source software CellProfiler is designed to help biologists working with images, whether a few or thousands. Researchers can download an online example workflow (that is, a “pipeline”) or create their own from scratch. Pipelines are easy to save, reuse, and share, helping improve scientific reproducibility. In this release, we’ve added the capability to find and measure objects in three-dimensional (3D) images. We’ve also made changes to CellProfiler’s underlying code to make it faster to run and easier to install, and we’ve added the ability to process images in the cloud and using neural networks (deep learning). We’ve also added more explanations to CellProfiler’s settings to help new users get started. We hope these changes will make CellProfiler an even better tool for current users and will provide new users better ways to get started doing quantitative image analysis.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pbio00:2005970

DOI: 10.1371/journal.pbio.2005970

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