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

Lookup NU author(s): Dr Liam Lachs, Dr James Guest



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.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.

Publication metadata

Author(s): Lachs L, Chong F, Beger M, East HK, Guest JR, Sommer B

Publication type: Article

Publication status: Published

Journal: Ecology and Evolution

Year: 2022

Volume: 12

Issue: 3

Pages: -

Print publication date: 14/03/2022

Online publication date: 14/03/2022

Acceptance date: 21/02/2022

Date deposited: 12/07/2022

ISSN (electronic): 2045-7758

Publisher: John Wiley and Sons Ltd


DOI: 10.1002/ece3.8724


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Funder referenceFunder name
725848Commission of the European Communities
NE/S007512/1Natural Environment Research Council (NERC)