The 3D skull 0-4 years: A validated, generative, statistical shape model

Eimear O' Sullivan; Lara S van de Lande; Anne-Jet C Oosting; Athanasios Papaioannou; N Owase Jeelani; Maarten J Koudstaal; Roman H Khonsari; David J Dunaway; Stefanos Zafeiriou; Silvia Schievano

doi:10.1016/j.bonr.2021.101154

The 3D skull 0-4 years: A validated, generative, statistical shape model

Bone Rep. 2021 Nov 29:15:101154. doi: 10.1016/j.bonr.2021.101154. eCollection 2021 Dec.

Authors

Affiliations

¹ Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
² Department of Computing, Imperial College London, London, UK.
³ Department of Oral and Maxillofacial Surgery, Erasmus Medical Centre, Rotterdam, the Netherlands.
⁴ Oral and Maxillofacial Surgery Department, Hospital Necker, Enfants Malades, Paris, France.

Abstract

Background: This study aims to capture the 3D shape of the human skull in a healthy paediatric population (0-4 years old) and construct a generative statistical shape model.

Methods: The skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population.

Results: The 3DMM of the paediatric skull 0-4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases.

Conclusion: This study presents a reliable statistical shape model of the paediatric skull 0-4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field.

Keywords: 3D morphable model; Morphometrics; Paediatric skull; Statistical shape model; Synthetic shapes.