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      CellProfiler 3.0: Next-generation image processing for biology

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Author summary

          The “big-data revolution” has struck biology: it is now common for robots to prepare cell samples and take thousands of microscopy images. Looking at the resulting images by eye would be extremely tedious, not to mention subjective. Thus, many biologists find they need software to analyze images easily and accurately. The third major release of our free open-source software CellProfiler is designed to help biologists working with images, whether a few or thousands. Researchers can download an online example workflow (that is, a “pipeline”) or create their own from scratch. Pipelines are easy to save, reuse, and share, helping improve scientific reproducibility. In this release, we’ve added the capability to find and measure objects in three-dimensional (3D) images. We’ve also made changes to CellProfiler’s underlying code to make it faster to run and easier to install, and we’ve added the ability to process images in the cloud and using neural networks (deep learning). We’ve also added more explanations to CellProfiler’s settings to help new users get started. We hope these changes will make CellProfiler an even better tool for current users and will provide new users better ways to get started doing quantitative image analysis.

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          Most cited references 26

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

          There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .
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            Objective Criteria for the Evaluation of Clustering Methods

             William Rand (1971)
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              Ebola virus. Two-pore channels control Ebola virus host cell entry and are drug targets for disease treatment.

              Ebola virus causes sporadic outbreaks of lethal hemorrhagic fever in humans, but there is no currently approved therapy. Cells take up Ebola virus by macropinocytosis, followed by trafficking through endosomal vesicles. However, few factors controlling endosomal virus movement are known. Here we find that Ebola virus entry into host cells requires the endosomal calcium channels called two-pore channels (TPCs). Disrupting TPC function by gene knockout, small interfering RNAs, or small-molecule inhibitors halted virus trafficking and prevented infection. Tetrandrine, the most potent small molecule that we tested, inhibited infection of human macrophages, the primary target of Ebola virus in vivo, and also showed therapeutic efficacy in mice. Therefore, TPC proteins play a key role in Ebola virus infection and may be effective targets for antiviral therapy.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Software
                Role: Data curationRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Software
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Software
                Role: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: Supervision
                Role: ResourcesRole: SoftwareRole: Writing – review & editing
                Role: InvestigationRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: SoftwareRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                3 July 2018
                July 2018
                3 July 2018
                : 16
                : 7
                Affiliations
                [1 ] Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
                [2 ] Skolkovo Institute of Science and Technology, Skolkovo, Moscow Region, Russia
                [3 ] Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia
                [4 ] Allen Institute for Cell Science, Seattle, Washington, United States of America
                National Cancer Institute, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Article
                pbio.2005970
                10.1371/journal.pbio.2005970
                6029841
                29969450
                © 2018 McQuin et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 4, Tables: 0, Pages: 17
                Product
                Funding
                National Institutes of Health https://projectreporter.nih.gov/project_info_description.cfm?aid=8761195&icde=39531171&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball= (grant number 2R01GM089652-05A1). Granted to AEC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Institutes of Health https://projectreporter.nih.gov/project_info_description.cfm?aid=9276910&icde=39531212&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball= (grant number 1R35GM122547-01). Granted to AEC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Allen Institute for Cell Science. Granted to AEC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Deutsche Forschungsgemeinschaft http://gepris.dfg.de/gepris/projekt/328668586 (grant number DFG research fellowship 5728). Granted to TB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Google Accelerated Sciences. Granted to AEC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Methods and Resources
                Engineering and Technology
                Signal Processing
                Image Processing
                Research and Analysis Methods
                Imaging Techniques
                Image Analysis
                Biology and Life Sciences
                Developmental Biology
                Embryology
                Blastocysts
                Science Policy
                Science and Technology Workforce
                Careers in Research
                Scientists
                Biologists
                People and Places
                Population Groupings
                Professions
                Scientists
                Biologists
                Research and Analysis Methods
                Specimen Preparation and Treatment
                Staining
                Cell Staining
                Computer and Information Sciences
                Computer Software
                Open Source Software
                Science Policy
                Open Science
                Open Source Software
                Computer and Information Sciences
                Software Engineering
                Software Tools
                Engineering and Technology
                Software Engineering
                Software Tools
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Custom metadata
                All data files are available from the Broad Bioimaging Benchmark Collection (BBBC) (accession number(s) BBBC022, BBBC024, BBBC032, BBBC033, BBBC034, BBBC035). Pipelines are publicly available at https://github.com/carpenterlab/2018_mcquin_PLOSBio.

                Life sciences

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