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      CellProfiler 4: improvements in speed, utility and usability

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          Abstract

          Background

          Imaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion of high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis of the resulting images is essential to effectively extract biological information from this data. CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements.

          Results

          Herein we describe CellProfiler 4, a new version of this software with expanded functionality. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimizations to reduce the time and cost associated with running common large-scale analysis pipelines.

          Conclusions

          CellProfiler 4 provides significantly improved performance in complex workflows compared to previous versions. This release will ensure that researchers will have continued access to CellProfiler’s powerful computational tools in the coming years.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12859-021-04344-9.

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          Most cited references13

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          NIH Image to ImageJ: 25 years of image analysis

          For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Is Open Access

            scikit-image: image processing in Python

            scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.
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              ilastik: interactive machine learning for (bio)image analysis

              We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.
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                Author and article information

                Contributors
                bcimini@broadinstitute.org
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                10 September 2021
                10 September 2021
                2021
                : 22
                : 433
                Affiliations
                [1 ]GRID grid.66859.34, Imaging Platform, , Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [2 ]GRID grid.507730.6, Software Department, , Allen Institute for Cell Science, ; Seattle, WA USA
                Author information
                http://orcid.org/0000-0001-9640-9318
                Article
                4344
                10.1186/s12859-021-04344-9
                8431850
                34507520
                bd68d3e3-60bb-4488-9798-5753420f568c
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 7 July 2021
                : 27 August 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, national institute of general medical sciences;
                Award ID: R35 GM122547
                Award ID: P41 GM135019
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000923, silicon valley community foundation;
                Award ID: 2018-192059
                Award ID: 2020-225720
                Award Recipient :
                Categories
                Software
                Custom metadata
                © The Author(s) 2021

                Bioinformatics & Computational biology
                image analysis,microscopy,image segmentation,image quantitation,bioimaging

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