Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.
Skolkovo Institute of Science and Technology, Skolkovo, Moscow Region, Russia.
PLoS Biol. 2018 Jul 3;16(7):e2005970. doi: 10.1371/journal.pbio.2005970. eCollection 2018 Jul.
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.
自 2005 年发布以来，CellProfiler 使科学界能够创建灵活的、模块化的图像分析管道。在这里，我们描述了 CellProfiler 3.0，这是该软件的一个新版本，支持对三维（3D）图像堆栈进行整体体积和平面分析，这在生物医学研究中越来越常见。CellProfiler 的基础架构得到了极大的改进，我们提供了一个基于云的、大规模图像处理的协议。新的插件可以在图像上运行预先训练的深度学习模型。CellProfiler 由生物学家设计和使用，通过一个记录良好的用户界面为研究人员提供强大的计算工具，使各个领域的生物学家都能够创建定量的、可重复的图像分析工作流程。
