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• W2953245870 abstract "Abstract In the post-genomics era, an emphasis has been placed on disentangling ‘genotype-phenotype’ connections so that the biological basis of complex phenotypes can be understood. However, our ability to efficiently and comprehensively characterize phenotypes lags behind our ability to characterize genomes. Here, we report a toolbox for fast and reproducible high-throughput dense phenotyping of 3D images. Given a target image, a rigid registration is first used to orient a template to the target surface, then the template is transformed further to fit the specific shape of the target using a non-rigid transformation model. As validation, we used N = 41 3D facial images registered with MeshMonk and manually landmarked at 19 locations. We demonstrate that the MeshMonk registration is accurate, with 0.62 mm as the average root mean squared error between the manual and automatic placements and no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks for comparison with traditional methods, MeshMonk allows for automatic dense phenotyping, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up an exciting avenue of study in assessing genomic and phenomic data to better understand the genetic contributions to complex morphological traits." @default.
• W2953245870 created "2019-06-27" @default.
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• W2953245870 date "2019-01-07" @default.
• W2953245870 modified "2023-10-18" @default.
• W2953245870 title "MeshMonk: Open-source large-scale intensive 3D phenotyping" @default.
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• W2953245870 doi "https://doi.org/10.1101/491639" @default.
• W2953245870 hasPublicationYear "2019" @default.
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