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SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images

1. Size is a biological characteristic that drives ecological processes from microscopic to geographic spatial scales, influencing cellular energetics, species fitness, population dynamics, and ecological interactions. Methods to measure size from images (e.g., proxies of body size, leaf area, and c...

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Autores principales: Lachs, Liam, Chong, Fiona, Beger, Maria, East, Holly K., Guest, James R., Sommer, Brigitte
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928875/
https://www.ncbi.nlm.nih.gov/pubmed/35342569
http://dx.doi.org/10.1002/ece3.8724
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author Lachs, Liam
Chong, Fiona
Beger, Maria
East, Holly K.
Guest, James R.
Sommer, Brigitte
author_facet Lachs, Liam
Chong, Fiona
Beger, Maria
East, Holly K.
Guest, James R.
Sommer, Brigitte
author_sort Lachs, Liam
collection PubMed
description 1. Size is a biological characteristic that drives ecological processes from microscopic to geographic spatial scales, influencing cellular energetics, species fitness, population dynamics, and ecological interactions. Methods to measure size from images (e.g., proxies of body size, leaf area, and cell area) occur along a gradient from manual approaches to fully automated technologies (e.g., machine learning). These methods differ in terms of time investment, expertise required, and data or resource availability. While manual methods can improve accuracy through human recognition, they can be labor intensive, highlighting the need for semi‐automated, and user‐friendly software or workflows to increase the efficiency of manual techniques. 2. Here, we present SizeExtractR, an open‐source workflow that enables faster extraction of size metrics from scaled images (e.g., each image includes a ruler) using semi‐automated protocols. It comprises a set of ImageJ macros to speed up size extraction and annotation, and an R‐package for the quality control of annotations, data collation, calibration, and visualization. 3. SizeExtractR extracts seven common size dimensions, including planar area, min/max diameter, and perimeter. Users can record additional categorical variables relating to their own study, for example species ID, by simply adding alphanumeric annotations to individual objects when prompted. Using a population size structure case study for hard corals as an example, we show how SizeExtractR was used to quantify the impact of mass coral bleaching on coral population dynamics. Lastly, the time saving benefit of using SizeExtractR was quantified during a series of timed image analyses, revealing up to a 49% reduction in image analysis time compared to a fully manual approach. 4. SizeExtractR automatically archives results, allowing re‐analysis of size extraction and promoting quality control and reproducibility. It has already been employed in marine and terrestrial sciences to assess population dynamics and demography, energy investment in eggs, and growth of nursery reared corals, with potential to be applied to a wide range of other research fields.
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spelling pubmed-89288752022-03-24 SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images Lachs, Liam Chong, Fiona Beger, Maria East, Holly K. Guest, James R. Sommer, Brigitte Ecol Evol Research Articles 1. Size is a biological characteristic that drives ecological processes from microscopic to geographic spatial scales, influencing cellular energetics, species fitness, population dynamics, and ecological interactions. Methods to measure size from images (e.g., proxies of body size, leaf area, and cell area) occur along a gradient from manual approaches to fully automated technologies (e.g., machine learning). These methods differ in terms of time investment, expertise required, and data or resource availability. While manual methods can improve accuracy through human recognition, they can be labor intensive, highlighting the need for semi‐automated, and user‐friendly software or workflows to increase the efficiency of manual techniques. 2. Here, we present SizeExtractR, an open‐source workflow that enables faster extraction of size metrics from scaled images (e.g., each image includes a ruler) using semi‐automated protocols. It comprises a set of ImageJ macros to speed up size extraction and annotation, and an R‐package for the quality control of annotations, data collation, calibration, and visualization. 3. SizeExtractR extracts seven common size dimensions, including planar area, min/max diameter, and perimeter. Users can record additional categorical variables relating to their own study, for example species ID, by simply adding alphanumeric annotations to individual objects when prompted. Using a population size structure case study for hard corals as an example, we show how SizeExtractR was used to quantify the impact of mass coral bleaching on coral population dynamics. Lastly, the time saving benefit of using SizeExtractR was quantified during a series of timed image analyses, revealing up to a 49% reduction in image analysis time compared to a fully manual approach. 4. SizeExtractR automatically archives results, allowing re‐analysis of size extraction and promoting quality control and reproducibility. It has already been employed in marine and terrestrial sciences to assess population dynamics and demography, energy investment in eggs, and growth of nursery reared corals, with potential to be applied to a wide range of other research fields. John Wiley and Sons Inc. 2022-03-14 /pmc/articles/PMC8928875/ /pubmed/35342569 http://dx.doi.org/10.1002/ece3.8724 Text en © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Lachs, Liam
Chong, Fiona
Beger, Maria
East, Holly K.
Guest, James R.
Sommer, Brigitte
SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images
title SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images
title_full SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images
title_fullStr SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images
title_full_unstemmed SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images
title_short SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images
title_sort sizeextractr: a workflow for rapid reproducible extraction of object size metrics from scaled images
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928875/
https://www.ncbi.nlm.nih.gov/pubmed/35342569
http://dx.doi.org/10.1002/ece3.8724
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