Enhanced head-skull shape learning using statistical modeling and topological features

Nguyen, Tan-Nhu; Tran, Vi-Do; Nguyen, Ho Quang; Nguyen, Duc-Phong; Dao, Tien Tuan

doi:10.1007/s11517-021-02483-y

Cited by 2 publications

(2 citation statements)

References 65 publications

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“…Craniofacial relationship investigation and facial-skeletal transformations remain a challenging task due to the complexity of geometric structures in the craniomaxillofacial (CMF) region and the significant difference between the geometric topologies of facial and skeletal shapes. It primarily involves 2 tasks: (1) facial prediction representing the estimation of external facial appearance from internal bony structures, which has been applied in VSP for CMF surgery, forensic identification, and anthropological research (Wang et al 2022;Ma et al 2023;Shui et al 2023); (2) skeletal prediction, representing the generation of internal bony structures from the facial shapes, which is of great significance for reference skeletal shape modeling in surgical planning, biomechanical head modeling, facial animation, and rehabilitation (Xiao et al 2021;Nguyen et al 2022). Many automated methods have been developed for facial and skeletal shape reconstruction including the facial soft-tissue thicknesses approach, homologues mesh model approach, and statistical model approach (Navic et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning–Based Facial and Skeletal Transformations for Surgical Planning

Bao,

Zhang,

Xiang

et al. 2024

J Dent Res

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The increasing application of virtual surgical planning (VSP) in orthognathic surgery implies a critical need for accurate prediction of facial and skeletal shapes. The craniofacial relationship in patients with dentofacial deformities is still not understood, and transformations between facial and skeletal shapes remain a challenging task due to intricate anatomical structures and nonlinear relationships between the facial soft tissue and bones. In this study, a novel bidirectional 3-dimensional (3D) deep learning framework, named P2P-ConvGC, was developed and validated based on a large-scale data set for accurate subject-specific transformations between facial and skeletal shapes. Specifically, the 2-stage point-sampling strategy was used to generate multiple nonoverlapping point subsets to represent high-resolution facial and skeletal shapes. Facial and skeletal point subsets were separately input into the prediction system to predict the corresponding skeletal and facial point subsets via the skeletal prediction subnetwork and facial prediction subnetwork. For quantitative evaluation, the accuracy was calculated with shape errors and landmark errors between the predicted skeleton or face with corresponding ground truths. The shape error was calculated by comparing the predicted point sets with the ground truths, with P2P-ConvGC outperforming existing state-of-the-art algorithms including P2P-Net, P2P-ASNL, and P2P-Conv. The total landmark errors (Euclidean distances of craniomaxillofacial landmarks) of P2P-ConvGC in the upper skull, mandible, and facial soft tissues were 1.964 ± 0.904 mm, 2.398 ± 1.174 mm, and 2.226 ± 0.774 mm, respectively. Furthermore, the clinical feasibility of the bidirectional model was validated using a clinical cohort. The result demonstrated its prediction ability with average surface deviation errors of 0.895 ± 0.175 mm for facial prediction and 0.906 ± 0.082 mm for skeletal prediction. To conclude, our proposed model achieved good performance on the subject-specific prediction of facial and skeletal shapes and showed clinical application potential in postoperative facial prediction and VSP for orthognathic surgery.

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Section: Introductionmentioning

confidence: 99%

Deep Learning–Based Facial and Skeletal Transformations for Surgical Planning

Bao,

Zhang,

Xiang

et al. 2024

J Dent Res

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“…The 3D reconstruction of an accurate face model is essential for providing reliable feedback. This is currently achieved by using medical imaging [ 6 ] and different sensors, such as Kinect or 3D scanners [ 7 , 8 ]. Thus, this allows for the analysis of the face with external (i.e., face deformation) and internal (i.e., facial muscle mechanics) feedback for the diagnosis and rehabilitation process of facial palsy and facial transplantation patients [ 9 ].In the past few decades, facial analysis has attracted great attention due to its numerous exploitations in human-computer interaction [ 10 , 11 ], animation for entertainment [ 12 , 13 , 14 ], and healthcare systems [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients

Nguyen

Dakpe

et al. 2022

Bioengineering

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The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image. However, the 3D face shape reconstruction of facial palsy patients is still a challenge, and this has not been investigated. The contribution of the present study is to apply these state-of-the-art methods to reconstruct the 3D face shape models of facial palsy patients in natural and mimic postures from one single image. Three different methods (3D Basel Morphable model and two 3D Deep Pre-trained models) were applied to the dataset of two healthy subjects and two facial palsy patients. The reconstructed outcomes were compared to the 3D shapes reconstructed using Kinect-driven and MRI-based information. As a result, the best mean error of the reconstructed face according to the Kinect-driven reconstructed shape is 1.5 ± 1.1 mm. The best error range is 1.9 ± 1.4 mm when compared to the MRI-based shapes. Before using the procedure to reconstruct the 3D faces of patients with facial palsy or other facial disorders, several ideas for increasing the accuracy of the reconstruction can be discussed based on the results. This present study opens new avenues for the fast reconstruction of the 3D face shapes of facial palsy patients from a single image. As perspectives, the best DL method will be implemented into our computer-aided decision support system for facial disorders.

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Enhanced head-skull shape learning using statistical modeling and topological features

Cited by 2 publications

References 65 publications

Deep Learning–Based Facial and Skeletal Transformations for Surgical Planning

Deep Learning–Based Facial and Skeletal Transformations for Surgical Planning

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients

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