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TOPHAT METHOD CELLPROFILER SOFTWARE

Mon Apr 13 15:50:59 2015: Image # 1, module IdentifyPrimaryObjects # 9: 7.35 sec Mon Apr 13 15:50:30 2015: Image # 1, module EnhanceOrSuppressFeatures # 8: 28.73 sec

^ "CellProfiler 4.0 Release: Improvements in speed, utility, and usability"."CellProfiler 3.0 release: faster, better, and 3D". ^ a b "What Is the Key Best Practice for Collaborating with a Computational Biologist?"."CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data". ^ Bray, Mark-Anthony Carpenter, Anne E."Pipeline for illumination correction of images for high-throughput microscopy".

TOPHAT METHOD CELLPROFILER SOFTWARE

"CellProfiler: free, versatile software for automated biological image analysis".

^ "CellProfiler - Bio-Formats 5.2.1 documentation".

"Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software".

^ a b Kamentsky L, Jones TR, Fraser A, Bray MA, Logan DJ, Madden KL, Ljosa V, Rueden C, Eliceiri KW, Carpenter AE (April 2011).

"CellProfiler: image analysis software for identifying and quantifying cell phenotypes".

^ Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM (2006).

The CellProfiler website contains a forum for discussion where new users can have their questions answered, usually by the creators of the project. Community īecause CellProfiler is a free, open-source project, anyone can develop their own image processing algorithms as a new module for CellProfiler and contribute it to the project. CellProfiler 4.0 was released in September 2020 and focused on speed, usability, and utility improvements with most notable example of migration to Python 3. Version 3.0, supporting volumetric analysis of 3D image stacks and optional deep learning modules, was released in October 2017. Originally developed in MATLAB, it was re-written in Python and released as CellProfiler 2.0 in 2010. It is currently developed and maintained by the Cimini Lab at the Imaging Platform of the Broad Institute. History ĬellProfiler was released in December 2005 by scientists from the Whitehead Institute for Biomedical Research and Massachusetts Institute of Technology. While designed and optimized for large numbers of two-dimensional images (the most common high-content screening image format), CellProfiler supports analysis of small-scale experiments and time-lapse movies.

ĬellProfiler interfaces with the high-performance scientific libraries NumPy and SciPy for many mathematical operations, the Open Microscopy Environment Consortium’s Bio-Formats library for reading more than 100 image file formats, ImageJ for use of plugins and macros, and ilastik for pixel-based classification. These measurements are accessible by using built-in viewing and plotting data tools, exporting in a comma-delimited spreadsheet format, or importing into a MySQL or SQLite database. Each of these steps are customizable by the user for their unique image assay.Ī wide variety of measurements can be generated for each identified cell or subcellular compartment, including morphology, intensity, and texture among others. Object identification ( segmentation) is performed through machine learning or image thresholding, recognition and division of clumped objects, and removal or merging of objects on the basis of size or shape. Specialized modules for illumination correction may be applied as pre-processing step to remove distortions due to uneven lighting. elegans worms) and then measure their properties of interest. Biologists typically use CellProfiler to identify objects of interest (e.g. CellProfiler can read and analyze most common microscopy image formats.