ArticleJournal of anatomy2017
Rapid automated landmarking for morphometric analysis of three-dimensional facial scans.
Article in Journal of anatomy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed.
- Comparing the coherent point drift algorithm to sliding semilandmark and weighted spherical harmonic whole-bone three-dimensional shape analyses.Journal of anatomy · 2026Article
- Assessing the application of landmark-free morphometrics to macroevolutionary analyses.BMC ecology and evolution · 2025Article
- Article
- Comparison Study of Extraction Accuracy of 3D Facial Anatomical Landmarks Based on Non-Rigid Registration of Face Template.Diagnostics (Basel, Switzerland) · 2023Article
- Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics.PLoS computational biology · 2023Article
- Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology.Annual review of genomics and human genetics · 2022Review
- Virtual Surgical Planning: Modeling from the Present to the Future.Journal of clinical medicine · 2021Review
- A landmark-free morphometrics pipeline for high-resolution phenotyping: application to a mouse model of Down syndrome.Development (Cambridge, England) · 2021Article
- A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics.Evolutionary biology · 2020Article
- Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images.Sensors (Basel, Switzerland) · 2020Article
- The effect of automated landmark identification on morphometric analyses.Journal of anatomy · 2019Article
- A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data.Insect systematics and diversity · 2019Review
- MeshMonk: Open-source large-scale intensive 3D phenotyping.Scientific reports · 2019Article
- Quantification of Facial Traits.Frontiers in genetics · 2019Review
- Body size and allometric variation in facial shape in children.American journal of physical anthropology · 2018Article
- Facial shape manifestations of growth faltering in Tanzanian children.Journal of anatomy · 2018Article
- Article
Corrections and comments
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Authors and funding
13 authors.
Funding
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. We 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. We 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. We further find that while our 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.