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MeshMonk: Open-source large-scale intensive 3D phenotyping

Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we...

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Autores principales: White, Julie D., Ortega-Castrillón, Alejandra, Matthews, Harold, Zaidi, Arslan A., Ekrami, Omid, Snyders, Jonatan, Fan, Yi, Penington, Tony, Van Dongen, Stefan, Shriver, Mark D., Claes, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6465282/
https://www.ncbi.nlm.nih.gov/pubmed/30988365
http://dx.doi.org/10.1038/s41598-019-42533-y
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author White, Julie D.
Ortega-Castrillón, Alejandra
Matthews, Harold
Zaidi, Arslan A.
Ekrami, Omid
Snyders, Jonatan
Fan, Yi
Penington, Tony
Van Dongen, Stefan
Shriver, Mark D.
Claes, Peter
author_facet White, Julie D.
Ortega-Castrillón, Alejandra
Matthews, Harold
Zaidi, Arslan A.
Ekrami, Omid
Snyders, Jonatan
Fan, Yi
Penington, Tony
Van Dongen, Stefan
Shriver, Mark D.
Claes, Peter
author_sort White, Julie D.
collection PubMed
description Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use. Given a target image, a template is first oriented, repositioned, and scaled to the target during a scaled rigid registration step, then transformed further to fit the specific shape of the target using a non-rigid transformation. As validation, we use n = 41 3D facial images to demonstrate that the MeshMonk registration is accurate, with 1.26 mm average error, across 19 landmarks, between placements from manual observers and using the MeshMonk toolbox. We also report no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks, the MeshMonk toolbox produces a dense mesh of vertices across the entire surface, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history.
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spelling pubmed-64652822019-04-18 MeshMonk: Open-source large-scale intensive 3D phenotyping White, Julie D. Ortega-Castrillón, Alejandra Matthews, Harold Zaidi, Arslan A. Ekrami, Omid Snyders, Jonatan Fan, Yi Penington, Tony Van Dongen, Stefan Shriver, Mark D. Claes, Peter Sci Rep Article Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use. Given a target image, a template is first oriented, repositioned, and scaled to the target during a scaled rigid registration step, then transformed further to fit the specific shape of the target using a non-rigid transformation. As validation, we use n = 41 3D facial images to demonstrate that the MeshMonk registration is accurate, with 1.26 mm average error, across 19 landmarks, between placements from manual observers and using the MeshMonk toolbox. We also report no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks, the MeshMonk toolbox produces a dense mesh of vertices across the entire surface, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history. Nature Publishing Group UK 2019-04-15 /pmc/articles/PMC6465282/ /pubmed/30988365 http://dx.doi.org/10.1038/s41598-019-42533-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
White, Julie D.
Ortega-Castrillón, Alejandra
Matthews, Harold
Zaidi, Arslan A.
Ekrami, Omid
Snyders, Jonatan
Fan, Yi
Penington, Tony
Van Dongen, Stefan
Shriver, Mark D.
Claes, Peter
MeshMonk: Open-source large-scale intensive 3D phenotyping
title MeshMonk: Open-source large-scale intensive 3D phenotyping
title_full MeshMonk: Open-source large-scale intensive 3D phenotyping
title_fullStr MeshMonk: Open-source large-scale intensive 3D phenotyping
title_full_unstemmed MeshMonk: Open-source large-scale intensive 3D phenotyping
title_short MeshMonk: Open-source large-scale intensive 3D phenotyping
title_sort meshmonk: open-source large-scale intensive 3d phenotyping
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6465282/
https://www.ncbi.nlm.nih.gov/pubmed/30988365
http://dx.doi.org/10.1038/s41598-019-42533-y
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