TY - JOUR
T1 - MeshMonk
T2 - Open-source large-scale intensive 3D phenotyping
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 D.
AU - Claes, Peter
N1 - Funding Information:
MeshMonk was developed with WebMonks (https://webmonks.vision), a Belgian startup that works as a bridge to highly qualified developers in third-world countries, and we are very grateful for their high-quality implementation and support. We also thank the many participants who have volunteered their time and all the past and present members of the Shriver and Claes labs, without whom we would have never been able to develop this toolbox or perform the research that it has contributed to. The sample collection and personnel involved in this work was supported by grants from the US National Institute of Justice (2008-DN-BX-K125), the US Department of Defense, the University of Illinois Interdisciplinary Innovation Initiative Research Grant, the Science Foundation of Ireland Walton Fellowship (04.W4/B643), the Penn State Center for Human Evolution and Development, the Research Fund KU Leuven (BOF-C1, C14/15/081) and the Research Program of the Fund for Scientific Research - Flanders (Belgium) (FWO, G078518N).
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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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U2 - 10.1038/s41598-019-42533-y
DO - 10.1038/s41598-019-42533-y
M3 - Article
C2 - 30988365
AN - SCOPUS:85064462310
SN - 2045-2322
VL - 9
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 6085
ER -