A statistical shape model for radiation-free assessment and classification of craniosynostosis
This work addresses the need for radiation-free diagnosis and data scarcity in craniosynostosis assessment, offering a domain-specific tool for medical imaging and classification.
The paper tackles the problem of assessing craniosynostosis, a craniofacial deformity, by building the first publicly available statistical 3D head model for infants under 1.5 years and a classification pipeline, achieving 97.8% accuracy in distinguishing between three classes and a control group.
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.