Similarly, for the training of machine learning models, manual annotations by experts are typically used as ground truth material.įor small datasets of a few dozen images, manual description of images can be performed by reporting the image identifier and qualitative descriptors in a simple spreadsheet. ![]() Therefore, for routine image data analysis and inspection, the qualitative description is usually performed manually. While automated methods for such qualitative description may exist, they usually require substantial effort for their implementation and validation. Qualitative descriptors, or keywords, can correspond to the presence or discrete count of features, the evaluation of quality criteria, or the assignment of images to specific categories. This routine task is shared by various scientific fields, for instance in biomedical research for the categorization of samples, in clinical imaging for image-based diagnostics, or in manufacturing for the description of object-properties. See the authors' detailed response to the review by Christian Tischer and Aliaksandr Halavatyi See the authors' detailed response to the review by Elisabet Teixido See the authors' detailed response to the review by Jan EglingerĪ common requirement of most imaging projects is to qualitatively describe images, either by assigning them to defined categories or by selecting a set of descriptive keywords. Plugins Several enhancements of the plugins previously suggested were implemented : - option to browse images in a directory - selection of the dimension for hyperstack browsing - "Add new category" button for the "button" and "checkbox" plugins - pop-up with keyboard shortcut message for buttons in the "button" plugin - "run measure" checkbox was moved to the initial configuration window - annotations are appended to any active table for ImageJ > 1.53g - Add a checkbox option to recover categories from an active table Both authors are former employees of DITABIS AG, Pforzheim, Germany and as of 2021 employees of ACQUIFER Imaging GmbH, Heidelberg, Germany exclusively. Manuscript - the introduction includes a brief review of existing annotation solutions and their limitations - Figure 1-3 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 - similarly the Uses cases section was simplified, there is no more dedicated paragraphs for the sunburst chart and deep learning - Previous Figure 4 is now 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. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji. 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. 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. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. ![]() In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. 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. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. 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. Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data.
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