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

Liam Lachs; Fiona Chong; Maria Beger; Holly K East; James R Guest; Brigitte Sommer

doi:10.1002/ece3.8724

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

Ecol Evol. 2022 Mar 14;12(3):e8724. doi: 10.1002/ece3.8724. eCollection 2022 Mar.

Authors

Liam Lachs¹, Fiona Chong^{2

3}, Maria Beger^{4

5}, Holly K East⁶, James R Guest¹, Brigitte Sommer^{7

8}

Affiliations

¹ School of Natural & Environmental Sciences Newcastle University Newcastle upon Tyne UK.
² Department of Biological & Marine Sciences University of Hull Hull UK.
³ Energy and Environment Institute University of Hull Hull UK.
⁴ School of Biology Faculty of Biological Sciences University of Leeds Leeds UK.
⁵ Centre for Biodiversity Conservation Science School of Biological Sciences The University of Queensland Brisbane Queensland Australia.
⁶ Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK.
⁷ School of Life Sciences University of Technology Sydney Sydney New South Wales Australia.
⁸ School of Life and Environmental Sciences The University of Sydney Sydney New South Wales Australia.

Abstract

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.

Keywords: coral reefs; image analysis; object dimensions; population dynamics; reproducibility; size frequency distributions; size metrics; time saving.