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

Lachs, Liam, Chong, Fiona, Beger, Maria, East, Holly, Guest, James R. and Sommer, Brigitte (2022) SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images. Ecology and Evolution, 12 (3). e8724. ISSN 2045-7758

Ecology and Evolution - 2022 - Lachs - SizeExtractR A workflow for rapid reproducible extraction of object size metrics.pdf - Published Version
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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.
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.
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.
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.

Item Type: Article
Additional Information: Funding information: This work was supported by the Natural Environment Research Council’s ONE Planet (NE/S007512/1) and Panorama (NE/S007458/1) Doctoral Training Partnerships to L.L. and F.C. respectively, the European Research Council Horizon 2020 project CORALASSIST (725848) to J.R.G.; funding from the Australian Research Council Centre of Excellence for Environmental Decisions (CE110001014) and an EU Marie Skłodowska-Curie Fellowship (TRIM-DLV-747102) to M.B.; and Postdoctoral Research Fellowships from the University of Technology Sydney and the University of Sydney to B.S.
Uncontrolled Keywords: coral reefs, image analysis, object dimensions, population dynamics, reproducibility, size frequency distributions, size metrics, time saving
Subjects: C900 Others in Biological Sciences
F800 Physical and Terrestrial Geographical and Environmental Sciences
L100 Economics
L900 Others in Social studies
Department: Faculties > Engineering and Environment > Geography and Environmental Sciences
Depositing User: Elena Carlaw
Date Deposited: 17 Mar 2022 17:46
Last Modified: 18 Mar 2022 08:00

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