Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]

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...

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Main Authors: Laurent S. V. Thomas, Franz Schaefer, Jochen Gehrig
Format: Article
Language:English
Published: F1000 Research Ltd 2021-02-01
Series:F1000Research
Online Access:https://f1000research.com/articles/9-1248/v2
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spelling doaj-845de1f67fd44d50acd6ce1ff450f9e32021-04-08T10:58:40ZengF1000 Research LtdF1000Research2046-14022021-02-01910.12688/f1000research.26872.254338Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]Laurent S. V. Thomas0Franz Schaefer1Jochen Gehrig2Acquifer Imaging GmbH, Heidelberg, GermanyDepartment of Pediatrics, University Children’s Hospital, Heidelberg, GermanyAcquifer Imaging GmbH, Heidelberg, GermanyQuantitative 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.https://f1000research.com/articles/9-1248/v2
collection DOAJ
language English
format Article
sources DOAJ
author Laurent S. V. Thomas
Franz Schaefer
Jochen Gehrig
spellingShingle Laurent S. V. Thomas
Franz Schaefer
Jochen Gehrig
Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]
F1000Research
author_facet Laurent S. V. Thomas
Franz Schaefer
Jochen Gehrig
author_sort Laurent S. V. Thomas
title Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]
title_short Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]
title_full Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]
title_fullStr Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]
title_full_unstemmed Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]
title_sort fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 2; peer review: 2 approved, 1 approved with reservations]
publisher F1000 Research Ltd
series F1000Research
issn 2046-1402
publishDate 2021-02-01
description 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.
url https://f1000research.com/articles/9-1248/v2
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