  The assessment of craniofacial deformities requires patient data which is
sparsely available. Statistical shape models provide realistic and synthetic
data enabling comparisons of existing methods on a common dataset.
  We build the first publicly available statistical 3D head model of
craniosynostosis patients and the first model focusing on infants younger than
1.5 years. We further present a shape-model-based classification pipeline to
distinguish between three different classes of craniosynostosis and a control
group on photogrammetric surface scans. To the best of our knowledge, our study
uses the largest dataset of craniosynostosis patients in a classification study
for craniosynostosis and statistical shape modeling to date.
  We demonstrate that our shape model performs similar to other statistical
shape models of the human head. Craniosynostosis-specific pathologies are
represented in the first eigenmodes of the model. Regarding the automatic
classification of craniosynostis, our classification approach yields an
accuracy of 97.8%, comparable to other state-of-the-art methods using both
computed tomography scans and stereophotogrammetry.
  Our publicly available, craniosynostosis-specific statistical shape model
enables the assessment of craniosynostosis on realistic and synthetic data. We
further present a state-of-the-art shape-model-based classification approach
for a radiation-free diagnosis of craniosynostosis.
