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                            Library & Multimedia
 Rapid automated landmarking for morphometric analysis of three-dimensional facial scans
NCJ Number
              307027

Author(s)
          Mao Li;
                        Joanne B. Cole;
                        Mange Manyama;
                        Jacinda R. Larson;
                        Denise K. Liberton;
                        Sheri L. Riccardi;
                        Tracey M. Ferrara;
                        Stephanie A. Santorico;
                        Jordan J. Bannister;
                        Nils D. Forkert;
                        Richard A. Spritz;
                        Washington Mio;
                        Benedikt Hallgrimsson
Date Published

2017
Length
              12 pages
Annotation
              This article presents research into the utility of automated phenotyping of anatomical imaging data for the creation of large, highly standardized datasets.
Abstract
              Automated phenotyping is essential for the creation of large, highly standardized datasets from anatomical imaging data. Such datasets can support large-scale studies of complex traits or clinical studies related to precision medicine or clinical trials. The authors have developed a method that generates three-dimensional landmark data that meet the requirements of standard geometric morphometric analyses. The method is robust and can be implemented without high-performance computing resources. The authors validated the method using both direct comparison to manual landmarking on the same individuals and also analyses of the variation patterns and outlier patterns in a large dataset of automated and manual landmark data. Direct comparison of manual and automated landmarks reveals that automated landmark data are less variable, but more highly integrated and reproducible. Automated data produce covariation structure that closely resembles that of manual landmarks. The authors further find that while thier method does produce some landmarking errors, they tend to be readily detectable and can be fixed by adjusting parameters used in the registration and control-point steps. Data generated using the method described here have been successfully used to study the genomic architecture of facial shape in two different genome-wide association studies of facial shape. (Published Abstract Provided)
      
  
          Additional Details
Grant Number(s)
        2013-DN-BX-K005


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Genetic Analysis of Facial Shape and Appearance
    
  
  
Date Published: January 1, 2017
    
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        Forensic sciences
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        Alternative genetic markers



  
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