Computer-assisted shape descriptors for skull morphology in craniosynostosis.

PURPOSE: Our aim was to develop a novel method for characterizing common skull deformities with high sensitivity and specificity, based on two-dimensional (2D) shape descriptors in computed tomography (CT) images.

METHODS

Between 2003 and 2014, 44 normal subjects and 39 infants with craniosynostosis (sagittal, 29; bicoronal, 10) enrolled for analysis. Mean age overall was 16 months (range, 1-120 months), with a male:female ratio of 56:29. Two reference planes, sagittal (S-plane: through top of lateral ventricle) and coronal (C-plane: at maximum dimension of fourth ventricle), were utilized to formulate three 2D shape descriptors (cranial index [CI], cranial radius index [CR], and cranial extreme spot index [CES]), which were then applied to S- and C-plane target images of both groups.

RESULTS

In infants with sagittal craniosynostosis, CI in S-plane (S-CI) usually was <1.0 (mean, 0.78; range, 0.67-0.95), with CR consistently at 3 and a characteristic CES pattern of two discrete hot spots oriented diagonally. In the bicoronal craniosynostosis subset, CI was >1.0 (mean 1.11; range, 1.04-1.25), with CR at -3 and a CES pattern of four discrete diagonally oriented hot spots. Scatter plots underscored the highly intuitive joint performance of CI and CES in distinguishing normal and deformed states. Altogether, these novel 2D shape descriptors enabled effective discrimination of sagittal and bicoronal skull deformities.

CONCLUSIONS

Newly developed 2D shape descriptors for cranial CT imaging enabled recognition of common skull deformities with statistical significance, perhaps providing impetus for automated CT-based diagnosis of craniosynostosis.