Laplace-Beltrami Refined Shape Regression Applied to Neck Reconstruction for Craniosynostosis Patients

• Published in: Current directions in biomedical engineering Vol. 7; no. 2; pp. 191 - 194
• Main Authors: Schaufelberger, Matthias, Kühle, Reinald, Weichel, Frederic, Wachter, Andreas, Hagen, Niclas, Ringwald, Friedemann, Eisenmann, Urs, Freudlsperger, Christian, Nahm, Werner
• Format: Journal Article
• Language: English
• Published: De Gruyter 01.10.2021
• Subjects: Craniosynostosis; Laplace-Beltrami; Posterior Shape Model; Scaphocephaly; Shape Reconstruction; Statistical Shape Modeling
• ISSN: 2364-5504
• DOI: 10.1515/cdbme-2021-2049

This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-based classification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, most SSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace- Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes.