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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  1. we think a more likely explanation is that greater education and outreach, as well as continued improvements in installation and access, will be needed before end users trust these tools enough to become excited about applying them to their own work.

    This is an interesting potential explanation for this decrease in interest in deep learning. Can you expand upon why you suspect this is the more likely explanation? Anything from the survey that supports this?

  2. ‘written tutorials’ are highly preferred by the imaging community

    I found this surprising at first and very helpful to know. It informs the level of detail that we should strive for when describing analysis methods.

  3. Figure 1

    This figure is a bit pixelated making it hard to see the text and numbers. If the resolution can be improved, it will help the reader want to study the figure.