He, Y. orcid.org/0000-0003-3464-7526, Cooney, C.R., Maddock, S. orcid.org/0000-0003-3179-0263 et al. (1 more author) (2023) Using pose estimation to identify regions and points on natural history specimens. PLOS Computational Biology, 19 (2). e1010933. ISSN 1553-734X
Abstract
A key challenge in mobilising growing numbers of digitised biological specimens for scientific research is finding high-throughput methods to extract phenotypic measurements on these datasets. In this paper, we test a pose estimation approach based on Deep Learning capable of accurately placing point labels to identify key locations on specimen images. We then apply the approach to two distinct challenges that each requires identification of key features in a 2D image: (i) identifying body region-specific plumage colouration on avian specimens and (ii) measuring morphometric shape variation in Littorina snail shells. For the avian dataset, 95% of images are correctly labelled and colour measurements derived from these predicted points are highly correlated with human-based measurements. For the Littorina dataset, more than 95% of landmarks were accurately placed relative to expert-labelled landmarks and predicted landmarks reliably captured shape variation between two distinct shell ecotypes (‘crab’ vs ‘wave’). Overall, our study shows that pose estimation based on Deep Learning can generate high-quality and high-throughput point-based measurements for digitised image-based biodiversity datasets and could mark a step change in the mobilisation of such data. We also provide general guidelines for using pose estimation methods on large-scale biological datasets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Deep learning; Birds; Taxonomy; Crabs; Biodiversity; Bird flight; Morphometry; Phenotypes |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) |
Funding Information: | Funder Grant number European Research Council 615709 NATURAL ENVIRONMENT RESEARCH COUNCIL NE/T01105X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Mar 2023 11:10 |
Last Modified: | 09 Nov 2023 02:30 |
Published Version: | http://dx.doi.org/10.1371/journal.pcbi.1010933 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pcbi.1010933 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197322 |
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Filename: Using pose estimation to identify regions and points on natural history specimens.pdf
Licence: CC-BY 4.0