3D Head Shape Quantification for Infants with and without Deformational Plagiocephaly

Abstract

Objective-We developed and tested three dimensional (3-D) indices for quantifying severity of deformational plagiocephaly (DP).Design-We evaluated the extent to which infants with and without DP (as determined by clinic referral and two experts' ratings) could be correctly classified.Participants-Infants ages 4-11 months, including 154 with diagnosed DP and 100 infants without a history of DP or other craniofacial condition. After excluding participants with discrepant expert ratings, data from 90 infants with DP and 50 infants without DP were retained.Measurements-Two-dimensional histograms of surface normal vector angles were extracted from 3-D mesh data and used to compute the severity scores below.Outcome measures-Left Posterior Flattening Score (LPFS), Right Posterior Flattening Score (RPFS), Asymmetry Score (AS), Absolute Asymmetry Score (AAS) and an approximation of a previously described 2-D measure, the Oblique Cranial Length Ratio (aOCLR). Two-dimensional histograms localized the posterior flatness for each participant.Analysis-We fit receiver operating characteristic curves and calculated the area under the curves (AUC) to evaluate the relative accuracy of DP classification using the above measures.Results-The AUC statistics were: AAS=91%; LPFS=97%, RPFS=91%; AS=99%, and aOCLR=79%.Conclusion-Novel 3-D-based plagiocephaly posterior severity scores provided better sensitivity and specificity in the discrimination of plagiocephalic and typical head shapes than the 2-D measurements provided by a close approximation of OCLR. These indices will allow for more precise quantification of the DP phenotype in future studies on the prevalence of this condition, which may lead to improved clinical care.

The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications

“…The first comparison is to a previous work of representing the whole face using a global saliency map [2]. The second comparison is to a global approach of representing the whole face, instead of only a specific facial region, using a 2D histogram of azimuth-elevation angles [1]. Table III shows the comparison results.…”

“…Results for the global saliency map [2], which uses curvature as its low-level feature, as a whole and with Adaboost-learning selected bins, are shown next. Results for the global 2D histogram of azimuth and elevation angles (with dimensions 24 × 24) [1] as a whole and with Adaboost-learning-selected bins, are given next. Using genetic programming to evolve the Adaboost-learning-selected bins of both the global saliency map and the global 2D histogram of azimuth and elevation angles further improved the F-measures.…”

“…Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2]. …”

Image analysis for biomedical and healthcare applications

“…This presentation will describe several of the novel techniques we have developed. For classification and quantification of the severity of a condition called plagiocephaly, which is a flatness of the back of the head, we developed a new feature called the azimuth-elevation histogram that allows us to both classify the condition and quantify the severity [1]. For the genetic syndrome 22q11.2 deletion syndrome, we have used the azimuth-elevation angle histogram among other features, with novel learning techniques to classify both the syndrome and multiple different facial manifestations [2].…”

Shape-Based Classification of 3D Facial Data to Support 22q11.2DS Craniofacial Research

“…A number of medical studies have sought both to quantify the facial features that are affected in these conditions and to help clinicians and researchers to better understand specific shape deformations. Examples of such conditions include: craniosynostosis, a condition caused by the premature fusion of cranial sutures due to biomechanical, environmental, hormonal or genetic factors [1,2]; 22q11.2 deletion syndrome, a genetically caused condition with its widely variable and often subtle facial feature dysmorphology [3]; and cleft lip and/or palate, a condition that causes abnormal facial development and is the subject of a large NIH-funded research effort called the FaceBase Consortium [4]. This article presents computational methodologies to aid craniofacial research by developing automated and objective measurements of craniofacial dysmorphologies.…”