Paula N Gonzalez1,2, Jimena Barbeito-Andrés1, Lucas A D'Addona2, Valeria Bernal2,3, S Ivan Perez2,3. 1. Instituto De Genética Veterinaria "Ing. Fernando N. Dulout", CONICET-Facultad De Ciencias Veterinarias, Universidad Nacional De La Plata, 1900, La Plata, Argentina. 2. División Antropología, Facultad De Ciencias Naturales Y Museo, Universidad Nacional de La Plata, La Plata, 1900, Argentina. 3. CONICET, Universidad Nacional De La Plata, 1900, La Plata, Argentina.
Abstract
OBJECTIVES: One of the biggest challenges in the study of complex morphologies is to adequately describe shape variation. Here, we assess how the random sampling of surface points automatically obtained performs, when compared with observer-guided sampling procedures, and also evaluate the effect of sliding surface points by bending energy and minimum Procrustes distance. MATERIAL AND METHODS: Three datasets comprising structures with disparate levels of complexity and intrasample variation are as follows: mouse molars, mouse brains, and primate endocasts. Different configurations of 3D coordinates on curves and surfaces were digitized from MRI images and CT scans using semi and fully automated procedures. Shape variables were obtained by Generalized Procrustes Superpositions before and after sliding the pseudolandmarks. Multivariate analyses were used to summarize and compare shape variation. RESULTS: For the primate endocast, the semiautomated and automated strategies yield similar ordinations of specimens. Conversely, the semiautomated strategy better discriminates molar shapes between mouse groups. Shape differences among specimens are not adequately represented by the PCs calculated with surface pseudolandmarks. This is improved when the points are converted into semilandmarks by a sliding criterion. DISCUSSION: Surface semilandmarks automatically obtained from 3D models are promising although they should be used with some caution in complex structures. This approach can be taken as complementary of semiautomated procedures which perform better for assessing shape variation in localized traits previously selected while automated procedures are suitable in studies aimed at comparing overall variation in shape and when there is no previous information about highly variable anatomical regions.
OBJECTIVES: One of the biggest challenges in the study of complex morphologies is to adequately describe shape variation. Here, we assess how the random sampling of surface points automatically obtained performs, when compared with observer-guided sampling procedures, and also evaluate the effect of sliding surface points by bending energy and minimum Procrustes distance. MATERIAL AND METHODS: Three datasets comprising structures with disparate levels of complexity and intrasample variation are as follows: mouse molars, mouse brains, and primate endocasts. Different configurations of 3D coordinates on curves and surfaces were digitized from MRI images and CT scans using semi and fully automated procedures. Shape variables were obtained by Generalized Procrustes Superpositions before and after sliding the pseudolandmarks. Multivariate analyses were used to summarize and compare shape variation. RESULTS: For the primate endocast, the semiautomated and automated strategies yield similar ordinations of specimens. Conversely, the semiautomated strategy better discriminates molar shapes between mouse groups. Shape differences among specimens are not adequately represented by the PCs calculated with surface pseudolandmarks. This is improved when the points are converted into semilandmarks by a sliding criterion. DISCUSSION: Surface semilandmarks automatically obtained from 3D models are promising although they should be used with some caution in complex structures. This approach can be taken as complementary of semiautomated procedures which perform better for assessing shape variation in localized traits previously selected while automated procedures are suitable in studies aimed at comparing overall variation in shape and when there is no previous information about highly variable anatomical regions.
Authors: Natasha S Vitek; Carly L Manz; Tingran Gao; Jonathan I Bloch; Suzanne G Strait; Doug M Boyer Journal: Ecol Evol Date: 2017-06-02 Impact factor: 2.912