Craniofacial malformations are among the most common structural malformations in humans. Researchers studying disorders of the craniofacial anatomy have many 3D imaging tools available to them, including computed tomography, magnetic resonance imaging, and 3D surface scans. Craniofacial researchers studying particular disorders are constructing large image databases of subjects in their studies. The Face Base Consortium will provide a central HUB for collection of data from numerous sites, enabling studies that would otherwise not be possible. Since shape is the critical factor in the classification of most craniofacial disorders, tools for analyzing 3D shape are essential to these studies. Quantitative shape descriptors allow for reproducible shape description, while similarity-based shape retrieval allows comparisons to be made between individuals or populations. The goal of this project is to develop tools for shape-based retrieval of 3D craniofacial image data.
The specific aims of the project are to: 1) develop software tools that produce quantitative representations of craniofacial anatomy that can assist in the study of mid-face hypoplasia and cleft lip and palate;2) develop tools for quantifying the similarity of craniofacial data between two individuals, between an individual and an average over a selected population, or between two populations;3) develop mechanisms for organization and retrieval of multimodality 3D craniofacial data based on their quantitative representations;and 4) design and implement a prototype system for Craniofacial Information Retrieval (CIR) that incorporates quantification, organization, and retrieval;evaluate it on 3D craniofacial data and make it available to the Face Base HUB. The design of these tools and a pilot system will lead to a general methodology that is immediately applicable to studies of mid-face hypoplasia, cleft lip and cleft palate, but is also scalable and modifiable to all craniofacial abnormalities.

Public Health Relevance

Cleft lip/palate, mid-face hypoplasia, and most disorders of the craniofacial complex are defined by anatomic differences in size or shape. Identifying pathogenesis is reliant on accurate phenotypic description. Our proposed work will improve the precision of phenotype classification of cleft and craniofacial disorders, while adding quantifiable measures allowing analysis of the relationship between severity and underlying cause.

National Institute of Health (NIH)
National Institute of Dental & Craniofacial Research (NIDCR)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZDE1-JH (24))
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Scholnick, Steven
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University of Washington
Biostatistics & Other Math Sci
Schools of Engineering
United States
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Aneja, D; Vora, S R; Camci, E D et al. (2015) Automated Detection of 3D Landmarks for the Elimination of Non-Biological Variation in Geometric Morphometric Analyses. Proc IEEE Int Symp Comput Based Med Syst 2015:78-83
Wu, Jia; Tse, Raymond; Shapiro, Linda G (2014) Automated face extraction and normalization of 3D Mesh Data. Conf Proc IEEE Eng Med Biol Soc 2014:750-3
Lam, Irma; Cunningham, Michael; Birgfeld, Craig et al. (2014) Quantification of skull deformity for craniofacial research. Conf Proc IEEE Eng Med Biol Soc 2014:758-61
Wu, Jia; Tse, Raymond; Shapiro, Linda G (2014) Learning to Rank the Severity of Unrepaired Cleft Lip Nasal Deformity on 3D Mesh Data. Proc IAPR Int Conf Pattern Recogn 2014:460-464
Liang, Shu; Kemelmacher-Shlizerman, Ira; Shapiro, Linda G (2014) 3D Face Hallucination from a Single Depth Frame. Proc Int Conf 3D Vis 2014:31-38
Lam, Irma; Cunningham, Michael; Speltz, Matthew et al. (2014) Classifying Craniosynostosis with a 3D Projection-Based Feature Extraction System. Proc IEEE Int Symp Comput Based Med Syst 2014:215-220
Mercan, Ezgi; Shapiro, Linda G; Weinberg, Seth M et al. (2013) The use of pseudo-landmarks for craniofacial analysis: a comparative study with L₁-regularized logistic regression. Conf Proc IEEE Eng Med Biol Soc 2013:6083-6
Rolfe, S M; Camci, E D; Mercan, E et al. (2013) A new tool for quantifying and characterizing asymmetry in bilaterally paired structures. Conf Proc IEEE Eng Med Biol Soc 2013:2364-7
Yang, Shulin; Shapiro, Linda; Cunningham, Michael et al. (2013) Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models. Med Image Comput Comput Assist Interv 7723:33-44
Liang, Shu; Wu, Jia; Weinberg, Seth M et al. (2013) Improved detection of landmarks on 3D human face data. Conf Proc IEEE Eng Med Biol Soc 2013:6482-5

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