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将成像用户与成像分析联系起来 - 一项社区调查。

Bridging imaging users to imaging analysis - A community survey.

机构信息

Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

Randall Centre for Cell and Molecular Biophysics and Research Management & Innovation Directorate, King's College London, London, UK.

出版信息

J Microsc. 2024 Dec;296(3):199-213. doi: 10.1111/jmi.13229. Epub 2023 Oct 12.

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 multichoice 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 familiarise 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. The results also showed less-than-expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. 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.

摘要

“连接成像用户与成像分析”调查于 2022 年由开放生物成像分析中心(COBA)、北美生物成像(BINA)和皇家显微镜学会数据分析成像分会(RMS DAIM)进行,旨在了解成像社区的需求。通过多项选择和开放式问题,该调查询问了人口统计学、图像分析经验、未来需求以及对工具开发人员和用户角色的建议。调查参与者来自生命和物理科学的不同角色和领域。据我们所知,这是首次尝试跨社区调查,以弥合物理和生命科学成像之间的知识差距。调查结果表明,受访者的首要需求是文档、有关图像分析工具使用的详细教程、用户友好的直观软件以及用于分割的更好解决方案,理想情况下是以适合其特定用例的格式提供。工具创建者建议用户熟悉图像分析的基础知识,提供持续的反馈,并报告在图像分析过程中遇到的问题,而用户希望获得更多文档和对工具友好性的重视。无论计算经验如何,人们都强烈倾向于通过“书面教程”来获取图像分析知识。我们还观察到,人们对“办公时间”的兴趣有所增加,希望在图像分析方法方面获得专家意见。调查结果还表明,在线讨论论坛在成像社区中用于解决图像分析问题的使用情况低于预期。令人惊讶的是,尽管人工智能在生物学中的应用越来越多,但调查受访者对深度学习/机器学习的兴趣也有所下降。此外,社区建议需要一个可用的图像分析工具及其应用的公共存储库。社区的意见和建议在此完整发布,将有助于图像分析工具创建和教育社区相应地进行设计和提供资源。

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