Quantitative evaluation of local head malformations from 3 dimensional photography: application to craniosynostosis

Liyun Tu; Antonio R. Porras; Albert Oh; Natasha Lepore; Graham C. Buck; Deki Tsering; Andinet Enquobahrie; Robert Keating; Gary F. Rogers; Marius George Linguraru

doi:10.1117/12.2512272

13 March 2019 Quantitative evaluation of local head malformations from 3 dimensional photography: application to craniosynostosis

Liyun Tu, Antonio R. Porras, Albert Oh, Natasha Lepore, Graham C. Buck, Deki Tsering, Andinet Enquobahrie, Robert Keating, Gary F. Rogers, Marius George Linguraru

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095035 (2019) https://doi.org/10.1117/12.2512272
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

The evaluation of head malformations plays an essential role in the early diagnosis, the decision to perform surgery and the assessment of the surgical outcome of patients with craniosynostosis. Clinicians rely on two metrics to evaluate the head shape: head circumference (HC) and cephalic index (CI). However, they present a high inter-observer variability and they do not take into account the location of the head abnormalities. In this study, we present an automated framework to objectively quantify the head malformations, HC, and CI from three-dimensional (3D) photography, a radiation-free, fast and non-invasive imaging modality. Our method automatically extracts the head shape using a set of landmarks identified by registering the head surface of a patient to a reference template in which the position of the landmarks is known. Then, we quantify head malformations as the local distances between the patient’s head and its closest normal from a normative statistical head shape multi-atlas. We calculated cranial malformations, HC, and CI for 28 patients with craniosynostosis, and we compared them with those computed from the normative population. Malformation differences between the two populations were statistically significant (p<0.05) at the head regions with abnormal development due to suture fusion. We also trained a support vector machine classifier using the malformations calculated and we obtained an improved accuracy of 91.03% in the detection of craniosynostosis, compared to 78.21% obtained with HC or CI. This method has the potential to assist in the longitudinal evaluation of cranial malformations after surgical treatment of craniosynostosis.

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Liyun Tu, Antonio R. Porras, Albert Oh, Natasha Lepore, Graham C. Buck, Deki Tsering, Andinet Enquobahrie, Robert Keating, Gary F. Rogers, and Marius George Linguraru "Quantitative evaluation of local head malformations from 3 dimensional photography: application to craniosynostosis", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095035 (13 March 2019); https://doi.org/10.1117/12.2512272

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