Automatic 3D modelling of craniofacial form
This work addresses the need for improved 3D modeling in craniofacial surgery, offering incremental advancements for medical professionals.
The paper tackles the problem of automatically building 3D models of craniofacial form for clinical assessment, presenting a method that uses machine learning and image analysis techniques to create more compact PCA-based models than previous approaches.
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.