Personalized assessment of craniosynostosis via statistical shape modeling

• Published in: Medical image analysis Vol. 18; no. 4; pp. 635 - 646
• Main Authors: Mendoza, Carlos S., Safdar, Nabile, Okada, Kazunori, Myers, Emmarie, Rogers, Gary F., Linguraru, Marius George
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2014
• Subjects: Computational anatomy; Computer-assisted diagnosis; Craniosynostoses - classification; Craniosynostoses - diagnostic imaging; Craniosynostosis; Graph-cut segmentation; Humans; Image Processing, Computer-Assisted; Shape analysis; Tomography, X-Ray Computed; Shape analysis; Graph-cut segmentation; Computer-assisted diagnosis; Computational anatomy; Craniosynostosis
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2014.02.008

[Display omitted] •We propose a technique for computational analysis of craniosynostosis from CT images.•We automatically identify different types of craniosynostosis using shape analysis.•Shape is analyzed on anatomical regions obtained with a novel segmentation technique.•Our analysis relies on an anatomical multi-atlas to account for normal variability.•We attain a 95.7% probability of correctly classifying a new subject. We present a technique for the computational analysis of craniosynostosis from CT images. Our fully automatic methodology uses a statistical shape model to produce diagnostic features tailored to the anatomy of the subject. We propose a computational anatomy approach for measuring shape abnormality in terms of the closest case from a multi-atlas of normal cases. Although other authors have tackled malformation characterization for craniosynostosis in the past, our approach involves several novel contributions (automatic labeling of cranial regions via graph cuts, identification of the closest morphology to a subject using a multi-atlas of normal anatomy, detection of suture fusion, registration using masked regions and diagnosis via classification using quantitative measures of local shape and malformation). Using our automatic technique we obtained for each subject an index of cranial suture fusion, and deformation and curvature discrepancy averages across five cranial bones and six suture regions. Significant differences between normal and craniosynostotic cases were obtained using these characteristics. Machine learning achieved a 92.7% sensitivity and 98.9% specificity for diagnosing craniosynostosis automatically, values comparable to those achieved by trained radiologists. The probability of correctly classifying a new subject is 95.7%.