Automatic 3D modelling of craniofacial form

TL;DR

This paper introduces an automated method for creating detailed 3D craniofacial models using machine learning and advanced surface analysis techniques, improving model compactness and clinical utility.

Contribution

The authors develop a novel pipeline combining landmarking, symmetry detection, and pose normalization for automatic craniofacial 3D modeling, outperforming traditional methods.

Findings

More compact PCA models with the new pose normalization

Successful clinical case study demonstrating practical utility

Enhanced accuracy in craniofacial shape analysis

Abstract

Three-dimensional models of craniofacial variation over the general population are useful for assessing pre- and post-operative head shape when treating various craniofacial conditions, such as craniosynostosis. We present a new method of automatically building both sagittal profile models and full 3D surface models of the human head using a range of techniques in 3D surface image analysis; in particular, automatic facial landmarking using supervised machine learning, global and local symmetry plane detection using a variant of trimmed iterative closest points, locally-affine template warping (for full 3D models) and a novel pose normalisation using robust iterative ellipse fitting. The PCA-based models built using the new pose normalisation are more compact than those using Generalised Procrustes Analysis and we demonstrate their utility in a clinical case study.