Human skull is an important body structure for jaw movement and facial mimic simulations. Surface head can be reconstructed using 3D scanners in a straightforward way. However, internal skull is challenging to be generated when only external information is available. Very few studies in the literature focused on the skull generation from outside head information, especially in a subject-specific manner with a complete skull. Consequently, this present study proposes a novel process for predicting a subject-specific skull with full details from a given head surface using a statistical shape modeling approach. Partial least squared regression (PLSR)-based method was used. A CT image database of 209 subjects (genders-160 males and 49 females; ages-34-88 years) was used for learning head-to-skull relationship. Heads and skulls were reconstructed from CT images to extract head/skull feature points, head/skull feature distances, head-skull thickness, and head/skull volume descriptors for the learning process. A hyperparameter turning process was performed to determine the optimal numbers of head/skull feature points, PLSR components, deformation control points, and appropriate learning strategies for our learning problem. Two learning strategies (point-to-thickness with/without volume descriptor and distance-to-thickness with/without volume descriptor) were proposed. Moreover, a 10-fold cross-validation procedure was conducted to evaluate the accuracy of the proposed learning strategies. Finally, the best and worst reconstructed skulls were analyzed based on the best learning strategy with its optimal parameters. The optimal number of head/skull feature points and deformation control points are 2300 and 1300 points, respectively. The optimal number of PLSR components ranges from 4 to 8 for all learning configurations. Cross-validation showed that grand means and standard deviations of the point-to-thickness, point-to-thickness with volumes, distance-to-thickness, and distance-to-thickness with volumes learning configurations are 2.48 ± 0.27 mm, 2.46 ± 0.19 mm, 2.46 ± 0.15 mm, and 2.48 ± 0.22 mm, respectively. Thus, the distance-to-thickness is the best learning configuration for our head-to-skull prediction problem. Moreover, the mean Hausdorff distances are 2.09 ± 0.15 mm and 2.64 ± 0.26 mm for the best and worst predicted skull, respectively. A novel head-to-skull prediction process based on the PLSR method was developed and evaluated. This process allows, for the first time, predicting 3D subject-specific human skulls from head surface information with a very good accuracy level. As perspective, the proposed head-to-skull prediction process will be integrated into our real-time computer-aided vision system for facial animation and rehabilitation. Graphical abstract.
Keywords: Cage-based skull deformation; Head-to-skull generation; Hyperparameter turning; Partial least square regression; Statistical shape modeling.