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Proceedings Paper

Computer-assisted quantification of surgical outcome in infants with sagittal craniosynostosis in 3D head CT images using mean normal skull model
Author(s): Min Jin Lee; Helen Hong; Kyu Won Shim
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Paper Abstract

Sagittal craniosynostosis is the most common craniosynostosis and the premature closure of the sagittal suture makes the skull shape biparietal narrow and elongated. Surgery in infants with sagittal craniosynostosis is a common treatment for correcting the shape of the deformed skull, but the evaluation of the morphological improvement of the skull before and after operation is made by the surgeon’s subjective judgment. Therefore, we propose an efficient method to quantify the shape of the skull before and after surgery based on the mean normal skull model to assess the surgical outcome. In the preprocessing step, to construct the skull model from the pre- and post-operative CT images, each skull model is constructed consisting of the outer surface of the skull. To distinguish individual cranial bones separated by sutures, mean normal skull model is composed of five cranial bones. In the skull model deformation step, to distinguish from the whole bone of the preoperative skull model to the regional bone, the mean normal skull model is deformed into a preoperative skull model, and the deformed mean normal skull model is again deformed into a postoperative skull model. In the regional skull shape index calculation step, to evaluate the degree of expansion and reduction of postoperative skull relative to the preoperative skull, the regional skull shape index is calculated. Experimental results showed that our regional skull shape index can quantify the degree of expansion and reduction of the postoperative skull relative to the preoperative skull of each cranial bone.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142Z (16 March 2020); doi: 10.1117/12.2551258
Show Author Affiliations
Min Jin Lee, Seoul Women's Univ. (Korea, Republic of)
Helen Hong, Seoul Women's Univ. (Korea, Republic of)
Kyu Won Shim, Yonsei Univ. College of Medicine (Korea, Republic of)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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