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      Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets

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          Abstract

          Understanding the structure–function relationship of cells and organelles in their natural context requires multidimensional imaging. As techniques for multimodal 3-D imaging have become more accessible, effective processing, visualization, and analysis of large datasets are posing a bottleneck for the workflow. Here, we present a new software package for high-performance segmentation and image processing of multidimensional datasets that improves and facilitates the full utilization and quantitative analysis of acquired data, which is freely available from a dedicated website. The open-source environment enables modification and insertion of new plug-ins to customize the program for specific needs. We provide practical examples of program features used for processing, segmentation and analysis of light and electron microscopy datasets, and detailed tutorials to enable users to rapidly and thoroughly learn how to use the program.

          Abstract

          This Community Page describes a freely available, open-source software that implements and integrates a range of manual and semi-automated segmentation tools for processing and quantifying light and electron microscopy data.

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          Most cited references18

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. 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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            An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.

            After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.
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              Metadata matters: access to image data in the real world

              Data sharing is important in the biological sciences to prevent duplication of effort, to promote scientific integrity, and to facilitate and disseminate scientific discovery. Sharing requires centralized repositories, and submission to and utility of these resources require common data formats. This is particularly challenging for multidimensional microscopy image data, which are acquired from a variety of platforms with a myriad of proprietary file formats (PFFs). In this paper, we describe an open standard format that we have developed for microscopy image data. We call on the community to use open image data standards and to insist that all imaging platforms support these file formats. This will build the foundation for an open image data repository.
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                Author and article information

                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                4 January 2016
                January 2016
                : 14
                : 1
                : e1002340
                Affiliations
                [001]Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Single Molecule Neuroscience Laboratory, Queensland Brain Institute, Brisbane, Australia

                Article
                PBIOLOGY-D-15-01478
                10.1371/journal.pbio.1002340
                4699692
                26727152
                2fdc917d-bd7f-42f4-9319-6b2fb3bfdd4a
                © 2016 Belevich 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

                History
                Page count
                Figures: 5, Tables: 0, Pages: 13
                Funding
                This work was supported by Biocenter Finland, Biological Imaging Network (EJ), Academy of Finland (projects 131650 and 1287975; EJ) and the Integrative Life Science Doctoral Program of the University of Helsinki (MJ and DK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Life sciences
                Life sciences

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