ImageJ生态系统在KNIME分析平台的集成。

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Frontiers in Computer Science Pub Date : 2020-03-01 Epub Date: 2020-03-17 DOI:10.3389/fcomp.2020.00008
Christian Dietz, Curtis T Rueden, Stefan Helfrich, Ellen T A Dobson, Martin Horn, Jan Eglinger, Edward L Evans, Dalton T McLean, Tatiana Novitskaya, William A Ricke, Nathan M Sherer, Andries Zijlstra, Michael R Berthold, Kevin W Eliceiri
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引用次数: 20

摘要

开源软件工具经常用于分析科学图像数据,因为它们在处理快速发展的成像技术方面具有灵活性和透明度。图像分析问题的复杂性经常需要许多工具一起使用,包括图像处理和分析、数据处理、机器学习和深度学习、结果的统计分析、可视化、异构但相关数据的相关性等等。然而,由于缺乏跨平台的集成,这些计算工具的开发和应用受到了阻碍。工具的集成超越了便利性,因为一个工具预测和适应每个用户当前和未来的需求是不切实际的。这个问题在生物图像分析领域得到了强调,研究人员正在迅速采用各种快速出现的方法。ImageJ是一个流行的开源图像分析平台,来自全球社区的贡献产生了数百个专门的例程,用于广泛的科学任务。ImageJ的优势在于它的可访问性和可扩展性,使研究人员可以轻松地改进软件来解决他们的图像分析任务。然而,ImageJ不是为开发复杂的端到端图像分析工作流而设计的。科学家经常被迫创建高度专业化且难以复制的脚本来编排单个软件片段,并覆盖图像数据集分析的整个生命周期。KNIME分析平台是一个用户友好的数据集成、分析和勘探工作流程系统,旨在处理与平台无关的计算环境中的大量异构数据,并已成功满足化学信息学和质谱等多个领域的复杂端到端需求。生物图像分析社区的类似需求导致了KNIME图像处理扩展的创建,该扩展将ImageJ集成到KNIME分析平台中,使研究人员能够开发可重复和可扩展的工作流程,集成了各种分析工具。在这里,我们介绍了用户和开发人员如何通过KNIME图像处理扩展来利用ImageJ生态系统,在KNIME工作流程中提供健壮和可扩展的图像分析。我们通过示例以及具有代表性的科学用例来说明这种集成的好处。
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Integration of the ImageJ Ecosystem in the KNIME Analytics Platform.

Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
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