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A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling

Authors :
Matthias Schaufelberger
Reinald Kühle
Andreas Wachter
Frederic Weichel
Niclas Hagen
Friedemann Ringwald
Urs Eisenmann
Jürgen Hoffmann
Michael Engel
Christian Freudlsperger
Werner Nahm
Source :
Diagnostics, Vol 12, Iss 7, p 1516 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data.

Details

Language :
English
ISSN :
20754418
Volume :
12
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.32533fde03c406c832d92b1dc6b3315
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics12071516