Abstract
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.
Highlights
In the field of bioimage analysis, a wide range of software tools have been published that tackle a wide range of bioimage processing and analysis tasks
We present four different integration mechanisms made possible by KNIME ImageJ integration, ranging from the very technical, requiring Java programming, to purely graphical workflow based, with no coding experience required: Execution of ImageJ Macro Code and Embedding Custom Java Code are developer and scripter focused; Nodes of the KNIME Image Processing Extension is user focused and described more fully in A Blueprint for Image Segmentation Using ImageJ and KNIME Analytics Platform; and Wrapping ImageJ Commands as KNIME Nodes is useful for users if a developer provides them with a SciJava command for use within KNIME
Using KNIME and the image processing workflow that we developed, we were able to measure this cytoplasmic-to-nuclear translocation over hundreds of cells
Summary
In the field of bioimage analysis, a wide range of software tools have been published that tackle a wide range of bioimage processing and analysis tasks. While these tools vary widely in their target audiences and implementations, many of them are open-source software (Eliceiri et al, 2012) due to the many advantages it brings to science (Cardona and Tomancak, 2012). The ImageJ application is a standalone image processing program in development since 1997 (Schneider et al, 2012) and is designed for scientific images It is highly extensible, with thousands of plugins and scripts for performing a wide variety of image processing tasks contributed by community developers. ImageJ and Fiji developers have invested much effort in improving the software’s internals, redesigning the ImageJ libraries to improve robustness, scalability, and reproducibility across a broader range of ndimensional scientific image data (Rueden et al, 2017, p. 2)
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