1-2 October 2020
Europe/Berlin timezone

JIPipe: a graphical batch-Processing language for ImageJ

1 Oct 2020, 14:00
20m
Main Session (Zoom)

Main Session

Zoom

Talk Session 2

Speaker

Ruman Gerst (HKI Jena)

Description

Bioimage analysis involves the development of novel algorithm pipelines that are easy to adapt to new data sets. A common way to create such tools is to develop scripts in languages like ImageJ macros [1], which requires programming experience and detailed knowledge of the available script functions. Alternatively, there are graphical programming languages like KNIME [2]⁠ and Icy protocols [3]⁠ that allow users to create complex pipelines by creating diagrams. Each tool comes with its own solutions for managing data sets that differ greatly in simplicity for beginners, verbosity in handling complex data sets, and how ImageJ is integrated into their own ecosystem. Here we present Java Image Processing Pipeline (JIPipe), a plugin for ImageJ that provides a graphical image processing language that not only includes functions from ImageJ, but also lets developers and non-programmers create new functions that can be used from within ImageJ. JIPipe introduces a new data model as hybrid between the easy-to-learn design of Icy and script-like style used by KNIME. Data is organized in tables where one column contains binary data such as images and additional text columns are used to annotate data. Such annotations can be used by algorithms to process data without the need for complicated control structures. JIPipe comes with over 400 image, table, plot and ROI processing algorithms, automated parallelization and result-exporting, and well-illustrated training material.

[1] C. T. Rueden et al., “ImageJ2: ImageJ for the next generation of scientific image data,” BMC Bioinformatics, vol. 18, no. 1, p. 529, 2017.
[2] M. R. Berthold et al., “{KNIME}: The {K}onstanz {I}nformation {M}iner,” 2007.
[3] F. De Chaumont et al., “Icy: an open bioimage informatics platform for extended reproducible research,” Nat. Methods, vol. 9, no. 7, pp. 690–696, 2012.

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