![]() ![]() Because 5% of CellProfiler-citing papers also used ImageJ ( ), we built a bridge to run ImageJ macros in the context of a CellProfiler pipeline. The Java/Python bridge allows CellProfiler 2.0 to load nearly 100 image formats via the Open Microscopy Environment Consortium's Bio-formats library ( ). We use Cython ( ) to implement computationally intensive algorithms, as well as bridge to precompiled libraries including Java via the Java Native Interface. Object-oriented design and professional software practices were integral to the porting effort, including version control, a continuous build process and the development of an extensive validation suite.ĬellProfiler 2.0 is designed to be extensible and interoperable its plug-in interface allows outside developers to write and distribute new CellProfiler modules. 2) and features ( Supplementary Table S2). 1 and Table 1), CellProfiler 2.0 compares favorably to CellProfiler 1.0 in terms of performance ( Supplementary Fig. While retaining the successful attributes of CellProfiler 1.0 ( Supplementary Fig. Robust infrastructure and interoperability: we redesigned the software's infrastructure while porting it from the proprietary MATLAB language to the open-source Python language, making use of the high-performance scientific libraries NumPy and SciPy (Oliphant, 2007). CellProfiler 2.0 improves upon the design of the original version, resulting in professionally engineered software with improved usability and functionality, as well as integration with other open-source image-related software. It is a modular, high-throughput, open-source biological image analysis package, and it won the 2009 Bio-IT World Best Practices Award in IT & Informatics. The diverse measurements generated by CellProfiler provide raw material for machine-learning algorithms that can identify challenging phenotypes ( Jones et al., 2009 Misselwitz et al., 2010 Ramo et al., 2009).ĬellProfiler fills a unique role in the software landscape. A variety of cell types have been analyzed, including budding yeast, Drosophila, mouse, rat and dozens of human cell types. counting cells, measuring staining intensities and scoring complex phenotypes with machine learning) and at many experimental scales (from a few to hundreds of thousands of images). CellProfiler has been used to measure individual cells, colonies of cells and whole organisms in a wide range of assays (e.g. These modular pipelines can be saved and shared with colleagues. This highlights the trend toward quantifying information in images regardless of experiment size.ĬellProfiler's interface lets researchers build customized chains of interoperable image analysis modules to identify and measure biological objects and features in images. CellProfiler was initially designed for high-throughput image analysis but is often used for small-scale projects. Roughly half of its users are outside the USA. In the 4 years since its publication ( Carpenter et al., 2006 Lamprecht et al., 2007), it has been rapidly adopted by the worldwide biological community and cited in more than 250 articles. With an interface designed by biologists and underlying algorithms developed by computer scientists, CellProfiler bridges the gap between advanced image analysis algorithms and scientists who lack computational expertise. CellProfiler is freely available, open-source software that enables researchers without training in computer programming to measure biological phenotypes quantitatively and automatically from thousands of images.
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