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.
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.
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