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      Bridging Imaging Users to Imaging Analysis - A community survey

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          Abstract

          The “Bridging Imaging Users to Imaging Analysis” survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), Bioimaging North America (BINA), and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multi-choice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs, and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents’ overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarize themselves with the fundamentals of image analysis, provide constant feedback, and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for ‘written tutorials’ to acquire knowledge on image analysis. We also observed that the interest in having ‘office hours’ to get an expert opinion on their image analysis methods has increased over the years. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly.

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          Most cited references15

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          Matplotlib: A 2D Graphics Environment

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            Array programming with NumPy

            Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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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

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                05 June 2023
                : 2023.06.05.543701
                Affiliations
                [1 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA
                [2 ]King’s College London, London, UK
                [3 ]University of Glasgow, Glasgow, UK
                [4 ]Francis Crick Institute, London, UK
                [5 ]Cardiff University, Cardiff, UK
                [6 ]University of Wisconsin-Madison, Madison, WI, USA
                Author notes
                [* ]To whom correspondence should be addressed; Contact details: Dr. Beth Cimini, Imaging Platform, Broad Institute, 415 Main St, Cambridge, MA 02142, bcimini@ 123456broadinstitute.org , Phone: 617-714-7000
                Article
                10.1101/2023.06.05.543701
                10274673
                37333353
                c3bcd7ba-51ab-4999-8884-f165499f06f8

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

                History
                Funding
                Funded by: Center for Open Bioimage Analysis (COBA), National Institute of General Medical Sciences
                Award ID: P41 GM135019
                Funded by: Chan Zuckerberg Initiative DAF, Silicon Valley Community Foundation
                Award ID: 2020-225720
                Funded by: The Francis Crick Institute, Cancer Research UK, UK Medical Research Council, Wellcome Trust
                Award ID: CC001999
                Funded by: Biotechnology and Biological Sciences Research Council (BBSRC)
                Award ID: BB/V006169/1
                Categories
                Article

                image analysis,survey,deep learning,life science,physical science

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