Craniosynostosis is characterized by the premature fusion of sutures in an infant skull. Diagnosis of craniofacial deformities such as craniosynostosis can include computed tomography (CT) or magnetic resonance tomography (MRT). With respect to the biological effects of radiation exposure and sedation in infants, such type of preoperative imaging should be minimized.

3D shape modeling of the head allows the estimation of craniofacial features which are able to approximate a variety of cranial shapes. Using statistical models and machine learning techniques, the resulting shape model can be used to estimate and classify different kinds of craniofacial deformities. 

This project aims to gain new insights in the description of shape modifications in the application of craniofacial deformities as well as insights in the estimation of deformities from 2D and 3D image data.