Background: Accurate dental landmark recognition is essential in orthodontics and prosthodontics applications but can be challenged by malocclusion and tooth wear. This study expends automated landmark recognition to include such complex cases, aiming to improve anatomical analysis and establish a treatment evaluation framework for Chinese patients. Methods: The study incorporated a dataset of 919 pairs of pretreatment intraoral scan (IOS) models. Initially, a subset of 100 IOS models was segmented and labeled using MeshLab software to generate 3-D dental grid data. Subsequently, 500 IOS models were meticulously labeled by three researchers to train and develop a neural network termed multiscale automated landmark recognition. This network reformulates the task of dental landmark recognition as a geodesic distance field problem on the tooth surface, effectively capturing both local and global geometric features essential for precise detection. The remaining 319 IOS models were then subjected to both manual labeling and automated recognition using the trained network, and the consistency between the two methods was evaluated. Finally, arch width measurements were compared between the two recognition approaches. Results: The neural network shows high concordance with manual labeling, with a mean Euclidean distance discrepancy of 0.375 mm, and no significant difference in arch width measurements between the two methods (P > 0.05). Conclusion: Based on the 0.500 mm clinically acceptable difference standard defined by the ABO objective grading system, the neural network developed in this study demonstrates superior performance in the automated recognition of IOS models exhibiting malocclusion and tooth wear.

Pseudo-labeling plays a critical role in semisupervised learning (SSL). Existing one-shot landmark localization approaches relied on unsupervised registration-based label transfer or similarity maps for pseudo-labeling, which has been combined with consistency regularization for detector training. However, unsupervised registration can misinterpret semantic correspondences, leading to noisy and unreliable pseudo-labels. Such pseudo labels mislead SSL and result in incorrect consensus in landmark localization. In this paper, we introduce a novel neural deformation prior-guided pseudo-labeling for one-shot craniofacial landmark localization. Specifically, we introduce a self-supervised volumetric registration model for attribute transfer and pseudo labeling, guided by a prior deformation field and sparse matched landmarks with confidence scoring. Moreover, we present learnable spatial correlation regularization to mitigate landmark perturbations during co-teaching of landmark detectors. By leveraging neural deformation priors and spatial correlation regularization, the proposed approach improves pseudo-labeling and enhances landmark-wise interdependencies, even in regions affected by image artifacts. Extensive experiments on clinical and public CT image datasets demonstrate our method achieves craniofacial landmark localization with performance gains over state-of-theart landmark detection methods. The source code is available at https://anonymous.4open.science/r/NPSCT-D3C1/.

The deep neural networks combined with the statistical shape model have enabled efficient deformable 2D-3D registration and recovery of 3D anatomical structures from a single radiograph. However, the recovered volumetric image tends to lack the volumetric fidelity of fine-grained anatomical structures and explicit consideration of cross-dimensional semantic correspondence. In this paper, we introduce a simple but effective solution for semantically-consistent deformable 2D-3D registration and detailed volumetric image recovery by inferring a voxel-wise registration field between the cone-beam computed tomography and a single lateral cephalometric radiograph (LC). The key idea is to refine the initial statistical model-based registration field with craniofacial structural details and semantic consistency from the LC. Specifically, our framework employs a self-supervised scheme to learn a voxel-level refiner of registration fields to provide fine-grained craniofacial structural details and volumetric fidelity. We also present a weakly supervised semantic consistency measure for semantic correspondence, relieving the requirements of volumetric image collections and annotations. Experiments showcase that our method achieves deformable 2D-3D registration with performance gains over state-of-the-art registration and radiograph-based volumetric reconstruction methods. The source code is available at https://github.com/Jyk-122/SC-DREG.

Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-level dependencies of muscle categories, enhancing the feature representation with noise-agnostic category knowledge. The region graph models both local and global dependencies of the candidate muscle regions of interest. The proposed BGR accommodates the high-level dependencies and enhances the region features in the presence of entangled soft tissue and image artifacts. We evaluated the proposed approach by segmenting masticatory muscles on clinically acquired CBCTs. Extensive experimental results show that the BGR effectively segments masticatory muscles with state-of-the-art accuracy.

This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node. The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees. A pseudo-leaf-splitting (PLS) algorithm is introduced to account for spatial relationships, which regularizes affinity measures and overcomes inconsistent leaf assign-ments. The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences. The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence. Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.

In this paper, we propose a fully convolutional network-based dense map from voxels to invertible pair of displacement vector fields regarding a template grid for the consistent voxel-wise correspondence. We parameterize the volumetric mapping using a convolutional network and train it in an unsupervised way by leveraging the spatial transformer to minimize the gap between the warped volumetric image and the template grid. Instead of learning the unidirectional map, we learn the nonlinear mapping functions for both forward and backward transformations. We introduce the combinational inverse constraints for the volumetric one-to-one maps, where the pairwise and triple constraints are utilized to learn the cycle-consistent correspondence maps between volumes. Experiments on both synthetic and clinically captured volumetric cone-beam CT (CBCT) images show that the proposed framework is effective and competitive against state-of-the-art deformable registration techniques.

Establishing dense correspondences of cone-beam computed tomography (CBCT) images is a crucial step for the attribute transfer and morphological variation assessment in clinical orthodontics. In this paper, a novel method, unsupervised spatially consistent clustering forest, is proposed to tackle the challenges for automatic supervoxel-wise correspondences of CBCT images. A complexity analysis of the proposed method with respect to the clustering hypotheses is provided with a data-dependent learning guarantee. The learning bound considers both the sequential tree traversals determined by questions stored in branch nodes and the clustering compactness of leaf nodes. A novel tree-pruning algorithm, guided by the learning bound, is also proposed to remove locally inconsistent leaf nodes. The resulting forest yields spatially consistent affinity estimations, thanks to the pruning penalizing trees with inconsistent leaf assignments and the combinational contextual feature channels used to learn the forest. A forest-based metric is utilized to derive the pairwise affinities and dense correspondences of CBCT images. The proposed method has been applied to the label propagation of clinically captured CBCT images. In the experiments, the method outperforms variants of both supervised and unsupervised forest-based methods and state-of-the-art label-propagation methods, achieving the mean dice similarity coefficients of 0.92, 0.89, 0.94, and 0.93 for the mandible, the maxilla, the zygoma arch, and the teeth data, respectively.

The 3D reconstruction is an essential step to measure the craniofacial morphological changes from the historical growth database with only 2D cephalograms. In this paper, we propose a novel regression-forest-based method to estimate the volumetric intensity images from a lateral cephalogram. The regression forest can produce a prediction of the volumetric craniofacial structure as a mixture of Gaussian by weighted aggregating the distributions from trees. The dense anatomical structure can be reconstructed with no time-consuming digitally-reconstructed-radiographs (DRR) in the online testing process. The experiments demonstrate the proposed method can reconstruct volumetric intensity images from the lateral cephalograms effectively.

Lateral cephalogram X-ray (LCX) images are essential to provide patientspecific morphological information of anatomical structures. The automatic annotation of anatomical structures in cephalograms has been performed in the biomedical engineering for nearly twenty years. Most systems only handle a portion of salient craniofacial landmark set [1, 2, 3]. Although model-based methods can produce a full set of markers [5, 7], the pattern fitting can fail to converge in blurry images. It is challenging to annotate LCX images with high fidelity. In this work, we propose a novel cephalogram sketcher system as shown in Fig. 1 for the automatic anatomical-structure annotation, especially for the blemished images due to structure overlappings and devicespecific distortions during projection. Firstly, we introduce an hierarchical extension of a pictorial model to detect anatomical structures. Secondly, the bimodal deep Boltzmann machine (DBM) is employed to sketch the structure contours. Specifically, the contour sketcher takes advantages of the path in the DBM to extract the contour definitions from the patch textures by alternating Gibbs sampling. Given a cephalogram I, the structure definition S, and the parameters Θ = (Θq,Θr) with respect to the intraand inter-layer correlations, the posterior probability distribution according to the Bayes rule is defined as P(S|I,Θ) ∝ P(I|S,Θ)P(S|Θ), where P(S|Θ) is a shape prior distribution. P(I|S,Θ) is the image likelihood given the hierarchical architecture and the model parameters. The likelihood can be factorized as a product of likelihoods of local structures.

With the objective of tracing out all craniofacial structures in parallel with landmarking on X-ray images in cephalometry for the first time, a novel approach is proposed to locate 262 feature points composed of 90 landmarks and 172 auxiliary points. Twelve landmarks are identified by classical image processing techniques and a pattern matching algorithm, and then are used to divide the craniofacial shape to ten independent regions according to the anatomical knowledge. For each region, principal component analysis is employed to statistically characterize its shape and the gray profile of every feature point in the training, and a modified active shape model is proposed for localization. We conclude with experiment results and some discussion.