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                            Library & Multimedia
 MeshMonk Open-source large-scale intensive 3D phenotyping
NCJ Number
              254102

Author(s)
          Julie D. White;
                        Alejandra Ortega-Castrillon;
                        Harold Matthews;
                        Arslan A. Zaidi;
                        Omid Ekrami;
                        Jonatan Snyders;
                        Yi Fan;
                        Tony Penington;
                        Stefan Van Dongen;
                        Mark D. Shriver;
                        Peter Claes
Date Published

2019
Length
              6 pages
Annotation
              This article reports on a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use.
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. Given a target image, with the proposed MeshMonk toolbox, 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, the authors used 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. The authors also report no variation in landmark position or centroid size significantly attributable to landmarking method used. Although 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 exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history. As validation, 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. The authors also report no variation in landmark position or centroid size significantly attributable to the landmarking method used. Although 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 provides exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2008-DN-BX-K125


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Identifying and Communicating Genetic Determinants of Facial Features:Practical Considerations in Forensic Molecular Photofitting
    
  
  
Date Published: January 1, 2019
    
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