MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation

Recently, deep learning-based tooth segmentation methods have been limited by the expensive and time-consuming processes of data collection and labeling. Achieving high-precision segmentation with limited datasets is critical. A viable solution to this entails fine-tuning pre-trained multiview-based models, thereby enhancing performance with limited data. However, relying solely on two-dimensional (2D) images for three-dimensional (3D) tooth segmentation can produce suboptimal outcomes because of occlusion and deformation, i.e., incomplete and distorted shape perception. To improve this fine-tuning-based solution, this paper advocates 2D-3D joint perception. The fundamental challenge in employing 2D-3D joint perception with limited data is that the 3D-related inputs and modules must follow a lightweight policy instead of using huge 3D data and parameter-rich modules that require extensive training data. Following this lightweight policy, this paper selects skeletons as the 3D inputs and introduces MSFormer, a novel method for tooth segmentation. MSFormer incorporates two lightweight modules into existing multiview-based models: a 3D-skeleton perception module to extract 3D perception from skeletons and a skeleton-image contrastive learning module to obtain the 2D-3D joint perception by fusing both multiview and skeleton perceptions. The experimental results reveal that MSFormer paired with large pre-trained multiview models achieves state-of-the-art performance, requiring only 100 training meshes. Furthermore, the segmentation accuracy is improved by 2.4%-5.5% with the increasing volume of training data.Comment: Under revie

3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg'22 challenge. The 3DTeethSeg'22 challenge code can be accessed at: https://github.com/abenhamadou/3DTeethSeg22_challengeComment: 29 pages, MICCAI 2022 Singapore, Satellite Event, Challeng

Geometrical modeling of complete dental shapes by using panoramic X-ray, digital mouth data and anatomical templates

In the field of orthodontic planning, the creation of a complete digital dental model to simulate and predict treatments is of utmost importance. Nowadays, orthodontists use panoramic radiographs (PAN) and dental crown representations obtained by optical scanning. However, these data do not contain any 3D information regarding tooth root geometries. A reliable orthodontic treatment should instead take into account entire geometrical models of dental shapes in order to better predict tooth movements. This paper presents a methodology to create complete 3D patient dental anatomies by combining digital mouth models and panoramic radiographs. The modeling process is based on using crown surfaces, reconstructed by optical scanning, and root geometries, obtained by adapting anatomical CAD templates over patient specific information extracted from radiographic data. The radiographic process is virtually replicated on crown digital geometries through the Discrete Radon Transform (DRT). The resulting virtual PAN image is used to integrate the actual radiographic data and the digital mouth model. This procedure provides the root references on the 3D digital crown models, which guide a shape adjustment of the dental CAD templates. The entire geometrical models are finally created by merging dental crowns, captured by optical scanning, and root geometries, obtained from the CAD templates

Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Automatic segmentation of 3D tooth models into individual teeth is an important step in orthodontic CAD systems. 3D tooth segmentation is a mesh instance segmentation task. Complex geometric features on the surface of 3D tooth models often lead to failure of tooth boundary detection, so it is difficult to achieve automatic and accurate segmentation by traditional mesh segmentation methods. We propose a novel solution to address this problem. We map a 3D tooth model isomorphically to a 2D harmonic parameter space and convert it into an image. This allows us to use a CNN to learn a highly robust image segmentation model to achieve automated and accurate segmentation of 3D tooth models. Finally, we map the image segmentation mask back to the 3D tooth model and refine the segmentation result using an improved Fuzzy Clustering-and-Cuts algorithm. Our method has been incorporated into an orthodontic CAD system, and performs well in practice

Craniofacial growth and development in modern humans and Neanderthals

This thesis assesses craniofacial growth, development and the dynamics of developmental interactions among cranial regions in modern humans and Neanderthals. To these ends, virtual segmentation, landmarking and Geometric Morphometrics (GM) are applied to an ontogenetic series of the whole crania of 68 H. sapiens and 12 H. neanderthalensis. First, the ontogenetic shape and form changes in the cranial vault, base and face are explored, and the locations and magnitudes of these changes are discussed. Secondly, allometric scaling is tested for differences among different age classes in the three regions of the cranium. In addition, the degree of covariation among these and how it changes over time is investigated.The study then focuses on interactions among facial regions. First, similar analyses as those used in the study of the cranium are applied to compare growth, development and covariation among parts of the face in different age classes. Additionally, a sample of 227 modern humans from 0 to 6 years of age is analysed using path analysis, to investigate the cascade of interactions and relative contributions of soft tissue and skeletal elements to the overall growth and development of the face. Last, the facial morphology of H. sapiens is compared to that of H. neanderthalensis and their ontogenetic trajectories are tested for divergence. Novel method registration-free colour maps are used to visualise regional changes during growth and development and to compare the morphologies of the two species. Covariation among facial elements is also compared to assess potential differences in developmental interactions. In modern humans, the results show that allometry and covariation change significantly among age classes and between cranial regions during ontogeny and that covariation is stronger in younger subadults than in older subadults and adults. Among modern humans, significantly divergent trajectories are observed between age classes during ontogeny in all three cranial regions. In the modern human face, allometric scaling also differs among age stages in each region. Interestingly, covariation among facial regions becomes progressively non-significant with time, with the exception of those including the nose and maxilla. Path analysis in modern humans shows a large contribution of the proxy used for nasal septum to the overall facial development. Soft tissues contribute only locally to the development of some skeletal elements of the face. Major aspects of the differences between adult modern humans and Neanderthals are already present in the youngest individuals. However, additional differences arise through differences in the degree of change in facial size and significantly divergent allometric trajectories. Analyses of covariation among Neanderthal facial regions suffer from small sample size but, where significant, suggest that the interactions among cranial components are similar to those in modern humans, with some differences

Morphological Differences of the Articulating Surfaces of Mandibular Condyles in C3H/HeJ and A/J Mice

Characterize the normal variation of the articulating surfaces of mandibular condyle morphologies during periods of growth within and between two strains of mice (A/J and C3H/HeJ) using 3D micro-CT analysis and determine which parts of the microanatomy of the articulating surfaces of the condyle are less susceptible to morphologic variation during skeletal growth. Methods: Cross sectional study utilized micro-CT scans of the condyles of two strains of mice (A/J and C3H/HeJ) at 3-5 wks, 6-8 wks and 9-11 wks of age. Virtual 3D surface models were created, analyzed and computed using shape analysis methods. Results: There is inter-strain variation in condyle morphologies among inbred strains and at each age group. For A/J condylar growth the greatest differences in morphologic change occurs between 3-5 weeks and 6-8 weeks of age with little change thereafter. For the C3H/HeJ strain condylar growth and morphology continued to change beyond 6-8 weeks of age. The anterior and the posterior surfaces of the condyles tended to vary greatest in morphology. Conclusions: Condyles of A/J inbred of mice reach a morphologic plateau around 6-8 weeks of age whereas C3H/HeJ inbred of mice condyles continue morphologic change and growth after 6-8 weeks. Inbred mice despite being isogenic still present shape differences in anatomical structures such as the condyle.Master of Scienc