A statistical shape model for radiation-free assessment and classification of craniosynostosis

• Main Authors: Schaufelberger, Matthias, Kühle, Reinald Peter, Wachter, Andreas, Weichel, Frederic, Hagen, Niclas, Ringwald, Friedemann, Eisenmann, Urs, Hoffmann, Jürgen, Engel, Michael, Freudlsperger, Christian, Nahm, Werner
• Format: Journal Article
• Language: English
• Published: 10.01.2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2201.03288

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.