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Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics
The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field fo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970057/ https://www.ncbi.nlm.nih.gov/pubmed/36656910 http://dx.doi.org/10.1371/journal.pcbi.1009061 |
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author | Thomas, Oshane O. Shen, Hongyu Raaum, Ryan L. Harcourt-Smith, William E. H. Polk, John D. Hasegawa-Johnson, Mark |
author_facet | Thomas, Oshane O. Shen, Hongyu Raaum, Ryan L. Harcourt-Smith, William E. H. Polk, John D. Hasegawa-Johnson, Mark |
author_sort | Thomas, Oshane O. |
collection | PubMed |
description | The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation, area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks, and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit. |
format | Online Article Text |
id | pubmed-9970057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99700572023-02-28 Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics Thomas, Oshane O. Shen, Hongyu Raaum, Ryan L. Harcourt-Smith, William E. H. Polk, John D. Hasegawa-Johnson, Mark PLoS Comput Biol Research Article The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation, area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks, and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit. Public Library of Science 2023-01-19 /pmc/articles/PMC9970057/ /pubmed/36656910 http://dx.doi.org/10.1371/journal.pcbi.1009061 Text en © 2023 Thomas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Thomas, Oshane O. Shen, Hongyu Raaum, Ryan L. Harcourt-Smith, William E. H. Polk, John D. Hasegawa-Johnson, Mark Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics |
title | Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics |
title_full | Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics |
title_fullStr | Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics |
title_full_unstemmed | Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics |
title_short | Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics |
title_sort | automated morphological phenotyping using learned shape descriptors and functional maps: a novel approach to geometric morphometrics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970057/ https://www.ncbi.nlm.nih.gov/pubmed/36656910 http://dx.doi.org/10.1371/journal.pcbi.1009061 |
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