Abstract: Inferring 3D Craniofacial Skeletal Shape from Facial Surface Geometry Using Reverse Engineering of a Forensic Tissue Depth Model
Notice bibliographique
Résumé
PURPOSE: The face and craniofacial skeleton (CFS) make up a complex 3D structure that is critical to human function and cosmesis. Traumatic injury to the CFS requires fracture treatment to both allow the recovery of mechanical function and forms a foundation for the restoration of soft tissue anatomy. CFS reconstruction aims to restore pre-injury appearance, however in severe injuries shape information for the skull and facial bones may be missing. This presents a particular challenge in bi-frontal injuries and pan-facial fractures where the mirror imaging of the intact side of the head cannot be used to guide reconstruction. The reconstruction of 3D facial surface geometry from pre-injury 2D photographs has recently been established through large scale morphable face modeling.1 As well, in forensic sciences, models with variable soft-tissue depths2 are used to determine face shape from skull geometry. This study aims to ‘reverse-engineer’ a forensics’ tissue depth model to determine pre-injury CFS shape from reconstructed 3D facial geometry. It is hypothesized that 3D forensics data can be used to fill in missing gaps in CFS geometry with sufficient accuracy to guide pre-operative planning for CFS reconstruction. METHODS: The forensics’ tissue depth model was applied to 3D facial geometries acquired through segmentation of head CT data. Age, sex and BMI were used as input parameters to guide the application of the forensics’ tissue depth model data to each face. The tissue depths between the face and CFS were determined by finding the Euclidian distance transform (nearest neighbor) employed by the original forensics study and via calculation using normal vectors generated from the face surface. Calculated tissue depth was evaluated against measured thickness on the head CT between the segmented CFS (bone) and the skin. RESULTS: Tissue depth determined by nearest neighbor and normal vector measurements yielded accurate reconstructions of the frontal and zygoma bones (within 1mm, or +/- 2 voxels). However, only the normal vector technique succeeded in estimating tissue depth in bone regions where the face and skull have differing concavity (i.e. eye sockets, maxilla). Agreement was more limited in the lower facial skeleton where greater variation of soft tissue structures occur. CONCLUSION: The reversed forensics tissue depth model was found to appropriately infer bony anatomy for the upper CFS from 3D face geometry. The 3D skull shaping provided by this work yields sufficient accuracy to warrant its inclusion into a translational pipeline of tools for pre-operative planning for CFS reconstruction. References: 1. Booth J. et al. Int J Comput Vis. 2017, 1–22. 2. Shrimpton S. et al. Forensic Sci. Int. 2014, 234, 103–110.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».