A Deep Learning Approach for Predicting Subject-Specific Human Skull Shape from Head Toward a Decision Support System for Home-Based Facial Rehabilitation

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

doi:10.1016/j.irbm.2022.05.005

Cited by 6 publications

(7 citation statements)

References 51 publications

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“…Published work developing a deep regression and long-short-term memory model to predict skull shape using features generated from CT head surface segmentations (using the TCIA dataset) yielded mean errors of up to 4 mm, suggesting additional work in this area is needed. 54…”

Section: Discussionmentioning

confidence: 99%

“…The intact skull shape could then be used as a template from which to generate intraoperative surgical guides, molds, or forming tools. Published work developing a deep regression and long–short-term memory model to predict skull shape using features generated from CT head surface segmentations (using the TCIA dataset) yielded mean errors of up to 4 mm, suggesting additional work in this area is needed 54 …”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence–Based Modeling Can Predict Face Shape Based on Underlying Craniomaxillofacial Bone

Arjmand,

Clement,

Hardisty

et al. 2023

Journal of Craniofacial Surgery

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Reconstructing facial deformities is often challenging due to the complex 3-dimensional (3D) anatomy of the craniomaxillofacial skeleton and overlying soft tissue structures. Bilateral injuries cannot benefit from mirroring techniques and as such preinjury information (eg, 2D pictures or 3D imaging) may be utilized to determine or estimate the desired 3D face shape. When patient-specific information is not available, other options such as statistical shape models may be employed; however, these models require registration to a consistent orientation which may be challenging. Artificial intelligence (AI) has been used to identify facial features and generate highly realistic simulated faces. As such, it was hypothesized that AI can be used to predict 3D face shape by learning its relationship with the underlying bone surface anatomy in a subject-specific manner. An automated image processing and AI modeling workflow using a modified 3D UNet was generated to estimate 3D face shape using the underlying bone geometry and additional metadata (eg, body mass index and age) obtained from 5 publicly available computed tomography imaging datasets. Visually, the trained models provided a reasonable prediction of the contour and geometry of the facial tissues. The pipeline achieved a validation dice=0.89 when trained on the combined 5 datasets, with the highest dice=0.925 achieved with the single HNSCC dataset. Estimated predefect facial geometry may ultimately be used to aid preoperative craniomaxillofacial surgical planning, providing geometries for intraoperative templates, guides, navigation, molds, and forming tools. Automated face shape prediction may additionally be useful in forensic studies to aid in the identification of unknown skull remains.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence–Based Modeling Can Predict Face Shape Based on Underlying Craniomaxillofacial Bone

Arjmand,

Clement,

Hardisty

et al. 2023

Journal of Craniofacial Surgery

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“…Means and standard deviations of muscle lengths, smiling strains, and kissing strains were also reported separately for all males and females. The database could be downloaded via the link [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation

et al. 2023

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Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction’s median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.

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“…Schlesinger et al [ 25 ] inferred the spatial coordinates of the same landmarks across the neurocranium from observable facial features using deep-learning models. Nguyen et al [ 43 ] predicted the human skull by extracting and computing multiple descriptors of head surface data and providing them to deep-learning models. Wu et al [ 44 ] learned the spatial distribution of the upper part of cranial bones and presented a deep-learning method to infer a complete cranial shape using a partial or damaged shape.…”

Section: Related Workmentioning

confidence: 99%

“…While Nguyen et al [ 43 ] rely on extracting a set of human-crafted features and descriptors and registering all heads into one common coordinate system, our method leverages arbitrary surface samples. In contrast to previous work, we perform automatic co-modeling and registration on a per-subject basis.…”

Section: Related Workmentioning

confidence: 99%

Scalp surface estimation and head registration using sparse sampling and 3D statistical models

Schlesinger,

Kundu,

Isaev

et al. 2024

Computers in Biology and Medicine

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A Deep Learning Approach for Predicting Subject-Specific Human Skull Shape from Head Toward a Decision Support System for Home-Based Facial Rehabilitation

Cited by 6 publications

References 51 publications

Artificial Intelligence–Based Modeling Can Predict Face Shape Based on Underlying Craniomaxillofacial Bone

Artificial Intelligence–Based Modeling Can Predict Face Shape Based on Underlying Craniomaxillofacial Bone

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation

Scalp surface estimation and head registration using sparse sampling and 3D statistical models

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