Fiji plugins for qualitative image annotations: routine analysis and application to image classification.

Jochen Gehrig,Laurent S. V. Thomas,Franz Schaefer

doi:10.12688/f1000research.26872.2

Abstract

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Highlights

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data
Manuscript - the introduction includes a brief review of existing annotation solutions and their limitations - Figure 1–Figure 3 have been updated to reflect the new plugin interfaces - Figure 4 has been replaced with an overview figure of the possible data-visualizations and applications - the Uses cases section was simplified, there is no more dedicated paragraphs for the sunburst chart and deep learning - Previous Figure 4 is available as Supplementary Figure 4 on Zenodo - A new data-visualization Fiji plugin for pie chart visualization was implemented - the DOI link to Zenodo was updated to always point to the latest version - The ‘competing interest’ statement was updated to reflect the current positions of Jochen Gehrig and Laurent Thomas
Implementation We developed a set of Fiji plugins for the assignment of single or multiple descriptive keywords to images, or image-regions outlined by regions of interest (ROIs)

Summary

15 Oct 2020 report report

Manuscript - the introduction includes a brief review of existing annotation solutions and their limitations - Figure 1–Figure 3 have been updated to reflect the new plugin interfaces - Figure 4 has been replaced with an overview figure of the possible data-visualizations and applications - the Uses cases section was simplified, there is no more dedicated paragraphs for the sunburst chart and deep learning - Previous Figure 4 is available as Supplementary Figure 4 on Zenodo - A new data-visualization Fiji plugin for pie chart visualization was implemented (see new supplementary Figure 2) - the DOI link to Zenodo was updated to always point to the latest version - The ‘competing interest’ statement was updated to reflect the current positions of Jochen Gehrig and Laurent Thomas Both authors are former employees of DITABIS AG, Pforzheim, Germany and as of 2021 employees of ACQUIFER Imaging GmbH, Heidelberg, Germany exclusively. We illustrate possible applications of the resulting standardized qualitative description for the visualization of data-distribution, or the training of supervised image classification models

Methods

Conclusion