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

Julie D. White, Alejandra Ortega-Castrillón, Harold Matthews, Arslan A. Zaidi, Omid Ekrami, Jonatan Snyders, Yi Fan, Tony Penington, Stefan Van Dongen, Mark Shriver, Peter Claes

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number6085
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

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Biological Science Disciplines
History
Phenotype

All Science Journal Classification (ASJC) codes

  • General

Cite this

White, J. D., Ortega-Castrillón, A., Matthews, H., Zaidi, A. A., Ekrami, O., Snyders, J., ... Claes, P. (2019). MeshMonk: Open-source large-scale intensive 3D phenotyping. Scientific reports, 9(1), [6085]. https://doi.org/10.1038/s41598-019-42533-y
White, Julie D. ; Ortega-Castrillón, Alejandra ; Matthews, Harold ; Zaidi, Arslan A. ; Ekrami, Omid ; Snyders, Jonatan ; Fan, Yi ; Penington, Tony ; Van Dongen, Stefan ; Shriver, Mark ; Claes, Peter. / MeshMonk : Open-source large-scale intensive 3D phenotyping. In: Scientific reports. 2019 ; Vol. 9, No. 1.
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White, JD, Ortega-Castrillón, A, Matthews, H, Zaidi, AA, Ekrami, O, Snyders, J, Fan, Y, Penington, T, Van Dongen, S, Shriver, M & Claes, P 2019, 'MeshMonk: Open-source large-scale intensive 3D phenotyping', Scientific reports, vol. 9, no. 1, 6085. https://doi.org/10.1038/s41598-019-42533-y

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; Claes, Peter.

In: Scientific reports, Vol. 9, No. 1, 6085, 01.12.2019.

Research output: Contribution to journalArticle

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AU - White, Julie D.

AU - Ortega-Castrillón, Alejandra

AU - Matthews, Harold

AU - Zaidi, Arslan A.

AU - Ekrami, Omid

AU - Snyders, Jonatan

AU - Fan, Yi

AU - Penington, Tony

AU - Van Dongen, Stefan

AU - Shriver, Mark

AU - Claes, Peter

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White JD, Ortega-Castrillón A, Matthews H, Zaidi AA, Ekrami O, Snyders J et al. MeshMonk: Open-source large-scale intensive 3D phenotyping. Scientific reports. 2019 Dec 1;9(1). 6085. https://doi.org/10.1038/s41598-019-42533-y