Most laboratories studying biological processes and human disease use light/fluorescence microscopes to image cells and other biological samples. There is strong and growing demand for software to analyze these images, as automated microscopes collect images faster than can be examined by eye and the information sought from images is increasingly quantitative and complex. We have begun to address this demand with CellProfiler, a versatile, open-source software tool for quantifying data from biological images, particularly in high-throughput experiments (www.cellprofiler.org). CellProfiler can extract valuable biological information from images quickly while increasing the objectivity and statistical power of assays. In the three years since its release, it has become widely used, having been downloaded more than 8,000 times by users in over 60 countries. Using CellProfiler's point-and-click interface, researchers build a customized chain of image analysis modules to identify and measure biological objects in images. The software evolved in an intensely collaborative and interdisciplinary research environment with dozens of ongoing projects and has been successfully applied to a wide range of biological samples and assays, from counting cells to scoring complex phenotypes by machine learning. To enable further biological imaging research, meet the needs of the growing user base, and expand the community that benefits from CellProfiler, we propose to improve its capabilities, interface, and support: First, we will add user-requested capabilities, leveraging existing open-source projects by interoperating with them where feasible. These new features will include object tracking in time-lapse movies, compatibility with additional file formats, new image processing algorithms, and expanded tools for quality control, performance evaluation, cluster computing, and workflow management. Second, we will improve the interface, increase processing speed, and simplify the addition of new features by refactoring and porting the MATLAB-based code to an open-source language and instituting proven software development practices. Third, we will provide user, educator, and developer support and outreach for CellProfiler. These activities will facilitate research in the scientific community and help guide usability improvements. These improvements to the only open-source software for modular, high-throughput biological image analysis will enable hundreds of NIH-funded laboratories to make high-impact biological discoveries from cell images across all disciplines within biology.
Most laboratories studying biological processes and human disease use microscopy to analyze cells and other samples. We will enable these researchers to rapidly and accurately extract numerical data from microscopy images by continuing to develop and support our user-friendly, open-source cell image analysis software, CellProfiler (www.cellprofiler.org).
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