Lymphatic endothelial transcription factor Tbx1 promotes an immunosuppressive microenvironment to facilitate post-myocardial infarction repair.

The heart is an autoimmune-prone organ. It is crucial for the heart to keep injury-induced autoimmunity in check to avoid autoimmune-mediated inflammatory disease. However, little is known about how injury-induced autoimmunity is constrained in hearts. Here, we reveal an unknown intramyocardial immunosuppressive program driven by Tbx1, a DiGeorge syndrome disease gene that encodes a T-box transcription factor (TF). We found induced profound lymphangiogenic and immunomodulatory gene expression changes in lymphatic endothelial cells (LECs) after myocardial infarction (MI). The activated LECs penetrated the infarcted area and functioned as intramyocardial immune hubs to increase the numbers of tolerogenic dendritic cells (tDCs) and regulatory T (Treg) cells through the chemokine Ccl21 and integrin Icam1, thereby inhibiting the expansion of autoreactive CD8+ T cells and promoting reparative macrophage expansion to facilitate post-MI repair. Mimicking its timing and implementation may be an additional approach to treating autoimmunity-mediated cardiac diseases.

Sparse-to-Local-Dense Matching for Geometry-Guided Correspondence Estimation.

Establishing reliable correspondences between two views is one of the most important components of various vision tasks. This paper proposes a novel sparse-to-local-dense (S2LD) matching method to conduct fully differentiable correspondence estimation with the prior from epipolar geometry. The sparse-to-local-dense matching asymmetrically establishes correspondences with consistent sub-pixel coordinates while reducing the computation of matching. The salient features are explicitly located, and the description is conditioned on both views with the global receptive field provided by the attention mechanism. The correspondences are progressively established in multiple levels to reduce the underlying re-projection error. We further propose a 3D noise-aware regularizer with differentiable triangulation. Additional guidance from 3D space is encoded by the regularizer in training to handle the supervision noise caused by the errors in camera poses and depth maps. The proposed method demonstrates outstanding matching accuracy and geometric estimation capability on multiple datasets and tasks.

Joint-Confidence-guided Multi-Task Learning for 3D Reconstruction and Understanding from Monocular Camera.

3D reconstruction and understanding from monocular camera is a key issue in computer vision. Recent learning-based approaches, especially multi-task learning, significantly achieve the performance of the related tasks. However a few works still have limitation in drawing loss-spatial-aware information. In this paper, we propose a novel Joint-confidence-guided network (JCNet) to simultaneously predict depth, semantic labels, surface normal, and joint confidence map for corresponding loss functions. In details, we design a Joint Confidence Fusion and Refinement (JCFR) module to achieve multi-task feature fusion in the unified independent space, which can also absorb the geometric-semantic structure feature in the joint confidence map. We use confidence-guided uncertainty generated by the joint confidence map to supervise the multi-task prediction across the spatial and channel dimensions. To alleviate the training attention imbalance among different loss functions or spatial regions, the Stochastic Trust Mechanism (STM) is designed to stochastically modify the elements of joint confidence map in the training phase. Finally, we design a calibrating operation to alternately optimize the joint confidence branch and the other parts of JCNet to avoid overfiting. The proposed methods achieve state-of-the-art performance in both geometric-semantic prediction and uncertainty estimation on NYU-Depth V2 and Cityscapes.

The mechanical effect of geometric design of attachments in invisible orthodontics.

INTRODUCTION: In invisible orthodontics, attachments are used with aligners to better control tooth movement. However, to what extent the geometry of the attachment can affect the biomechanical properties of the aligner is unknown. This study aimed to determine the biomechanical effect of attachment geometry on orthodontic force and moment using 3-dimensional finite element analysis. METHODS: A 3-dimensional model of mandibular teeth, periodontal ligaments, and the bone complex was employed. Rectangular attachments with systematic size variations were applied to the model with corresponding aligners. Fifteen pairs were created to move the lateral incisor, canine, first premolar, and second molar mesially for 0.15 mm, respectively. The resulting orthodontic forces and moments were analyzed to compare the effect of attachment size. RESULTS: Expansion in the attachment size showed a continuous increase in force and moment. Considering the attachment size, the moment increased more than the force, resulting in a slightly higher moment-to-force ratio. Expanding the length, width, or thickness of the rectangular attachment by 0.50 mm increases the force and moment up to 23 cN and 244 cN-mm, respectively. The force direction was closer to the desired movement direction with larger attachment sizes. CONCLUSIONS: Based on the experimental results, the constructed model successfully simulates the effect of the size of attachments. The larger the size of the attachment, the greater the force and moment, and the better the force direction. The appropriate force and moment for a particular clinical patient can be obtained by choosing the right attachment size.

Fully automated cardiac MRI segmentation using dilated residual network.

PURPOSE: Cardiac ventricle segmentation from cine magnetic resonance imaging (CMRI) is a recognized modality for the noninvasive assessment of cardiovascular pathologies. Deep learning based algorithms achieved state-of-the-art result performance from CMRI cardiac ventricle segmentation. However, most approaches received less attention at the bottom layer of UNet, where main features are lost due to pixel degradation. To increase performance, it is important to handle the bottleneck layer of UNet properly. Considering this problem, we enhanced the performance of main features at the bottom layer of network. METHOD: We developed a fully automatic pipeline for segmenting the right ventricle (RV), myocardium (MYO), and left ventricle (LV) by incorporating short-axis CMRI sequence images. We propose a dilated residual network (DRN) to capture the features at full resolution in the bottleneck of UNet. Thus, it significantly increases spatial and temporal information and maintains the localization accuracy. A data-augmentation technique is employed to avoid overfitting and class imbalance problems. Finally, output from each expanding path is added pixel-wise to improve the training response. RESULTS: We used and evaluated our proposed method on automatic cardiac diagnosis challenge (ACDC). The test set consists of 50 patient records. The overall dice similarity coefficient (DSC) we achieved for our model is 0.924 ± 0.03, 0.907 ± 0.01, and 0.949 ± 0.05 for RV, MYO, and LV, respectively. Similarly, we obtained hausdorff distance (HD) scores of 10.09 ± 0.01, 7.25 ± 0.05, and 6.86 ± 0.02 mm for RV, MYO, and LV, respectively. The results show superior performance and outperformed state-of-the-art methods in terms of accuracy and reached expert-level segmentation. Consequently, the overall DSC and HD result improved by 1.0% and 1.5%, respectively. CONCLUSION: We designed a dilated residual UNet (DRN) for cardiac ventricle segmentation using short-axis CMRI. Our method has the advantage of restoring and capturing spatial and temporal information by expanding the receptive field without degrading the image main features in the bottleneck of UNet. Our method is highly accurate and quick, taking 0.28 s on average to process 2D MR images. Also, the network was designed to work on predictions of individual MR images to segment the ventricular region, for which our model outperforms many state-of-the-art methods.

[Comprehensive evaluation method of real-time non-reference ultrasound image involving soft tissue deformation].

Ultrasound guided percutaneous interventional therapy has been widely used in clinic. Aiming at the problem of soft tissue deformation caused by probe contact force in robot-assisted ultrasound-guided therapy, a real-time non-reference ultrasound image evaluation method considering soft tissue deformation is proposed. On the basis of ultrasound image brightness and sharpness, a multi-dimensional ultrasound image evaluation index was designed, which incorporated the aggregation characteristics of the organization. In order to verify the effectiveness of the proposed method, ultrasound images of four different models were collected for experiments, including prostate phantom, phantom with cyst, pig liver tissue, and pig liver tissue with cyst. In addition, the correlation between subjective and objective evaluations was analyzed based on Spearman's rank correlation coefficient. Experimental results showed that the average evaluation time of a single image was 68.8 milliseconds. The evaluation time could satisfy real-time applications. The proposed method realizes the effective evaluation of real-time ultrasound image quality in robot-assisted therapy, and has good consistency with the evaluation of supervisors.

LSTformer: Long Short-Term Transformer for Real Time Respiratory Prediction.

Since the tumor moves with the patient's breathing movement in clinical surgery, the real-time prediction of respiratory movement is required to improve the efficacy of radiotherapy. Some RNN-based respiratory management methods have been proposed for this purpose. However, these existing RNN-based methods often suffer from the degradation of generalization performance for a long-term window (such as 600 ms) because of the structural consistency constraints. In this paper, we propose an innovative Long Short-term Transformer (LSTformer) for long-term real-time accurate respiratory prediction. Specifically, a novel Long-term Information Enhancement module (LIE) is proposed to solve the performance degradation under a long window by increasing the long-term memory of latent variables. A lightweight Transformer Encoder (LTE) is proposed to satisfy the real-time requirement via simplifying the architecture and limiting the number of layers. In addition, we propose an application-oriented data augmentation strategy to generalize our LSTformer to practical application scenarios, especially robotic radiotherapy. Extensive experiments on our augmented dataset and publicly available dataset demonstrate the state-of-the-art performance of our method on the premise of satisfying the real-time demand.

Intelligent cataract surgery supervision and evaluation via deep learning.

PURPOSE: To assess the performance of a deep learning (DL) algorithm for evaluating and supervising cataract extraction using phacoemulsification with intraocular lens (IOL) implantation based on cataract surgery (CS) videos. MATERIALS AND METHODS: DeepSurgery was trained using 186 standard CS videos to recognize 12 CS steps and was validated in two datasets that contained 50 and 21 CS videos, respectively. A supervision test including 50 CS videos was used to assess the DeepSurgery guidance and alert function. In addition, a real-time test containing 54 CSs was used to compare the DeepSurgery grading performance to an expert panel and residents. RESULTS: DeepSurgery achieved stable performance for all 12 recognition steps, including the duration between two pairs of adjacent steps in internal validation with an ACC of 95.06% and external validations with ACCs of 88.77% and 88.34%. DeepSurgery also recognized the chronology of surgical steps and alerted surgeons to order of incorrect steps. Six main steps are automatically and simultaneously quantified during the evaluation process (centesimal system). In a real-time comparative test, the DeepSurgery step recognition performance was robust (ACC of 90.30%). In addition, DeepSurgery and an expert panel achieved comparable performance when assessing the surgical steps (kappa ranged from 0.58 to 0.77). CONCLUSIONS: DeepSurgery represents a potential approach to provide a real-time supervision and an objective surgical evaluation system for routine CS and to improve surgical outcomes.

Surgical Treatment of Congenital Dermal Sinus: An Experience of 56 Cases.

OBJECTIVE: This study aims to assess the impact of early diagnosis and surgery on children with congenital dermal sinus, investigate the relationship between MRI findings and extent of surgical exploration, and summarize our clinical experience with the surgical management in cases with central nervous system (CNS) infection. METHODS: The skin features, preoperative MRI images, intraoperative findings, postoperative pathological characteristics, and prognoses of 56 children with congenital dermal sinus were analyzed retrospectively. RESULTS: All the children had a pinpoint ostium in the skin, and 52 out of the 56 children (92.9%) had intraspinal dermoid cysts or epidermoid cysts. Before surgery, MRI did not show intraspinal lesions in 13 children, and surgery revealed intradural lesions in 9 of these children (69.2%). Among 46 children without CNS infection, 16 children had neurological impairment before surgery. After surgery, recovery was complete in 36 children, partial in 9 children, and absent in 3 children. All children with CNS infection had neurological impairment before surgery. After surgery, the condition improved in 8 children and exacerbated in 2 children. Children without CNS infection had statistically significantly better prognosis than children with CNS infection (p = 0.03). CONCLUSION: A pinpoint ostium in the dorsal midline is the characteristic feature of congenital dermal sinus. In cases without intraspinal lesions on MRI, the spinal canal should be explored intraoperatively to ensure complete removal of the lesion and prevent recurrences. In cases without CNS infection, early diagnosis and timely surgery are beneficial to the recovery of nerve function.

Segment-Based Disparity Refinement With Occlusion Handling for Stereo Matching.

In this paper, we propose a disparity refinement method that directly refines the winner-take-all (WTA) disparity map by exploring its statistical significance. According to the primary steps of the segment-based stereo matching, the reference image is over-segmented into superpixels and a disparity plane is fitted for each superpixel by an improved random sample consensus (RANSAC). We design a two-layer optimization to refine the disparity plane. In the global optimization, mean disparities of superpixels are estimated by Markov random field (MRF) inference, and then, a 3D neighborhood system is derived from the mean disparities for occlusion handling. In the local optimization, a probability model exploiting Bayesian inference and Bayesian prediction is adopted and achieves second-order smoothness implicitly among 3D neighbors. The two-layer optimization is a pure disparity refinement method because no correlation information between stereo image pairs is demanded during the refinement. Experimental results on the Middlebury and KITTI datasets demonstrate that the proposed method can perform accurate stereo matching with a faster speed and handle the occlusion effectively. It can be indicated that the "matching cost computation + disparity refinement" framework is a possible solution to produce accurate disparity map at low computational cost.

Simulation of orthodontic force of archwire applied to full dentition using virtual bracket displacement method.

OBJECTIVE: Orthodontic force simulation of tooth provides important guidance for clinical orthodontic treatment. However, previous studies did not involve the simulation of orthodontic force of archwire applied to full dentition. This study aimed to develop a method to simulate orthodontic force of tooth produced by loading a continuous archwire to full dentition using finite element method. METHOD: A three-dimensional tooth-periodontal ligament-bone complex model of mandible was reconstructed from computed tomography images, and models of brackets and archwire were built. The simulation was completed through two steps. First, node displacements of archwire before and after loading were estimated through moving virtual brackets to drive archwire deformation. Second, the obtained node displacements were loaded to implement the loading of archwire, and orthodontic force was calculated. An orthodontic force tester (OFT) was used to measure orthodontic force in vitro for the validation. RESULTS: After the simulation convergence, archwire was successfully loaded to brackets, and orthodontic force of teeth was obtained. Compared with the measured orthodontic force using the OFT, the absolute difference of the simulation results ranged from 0.5 to 22.7 cN for force component and ranged from 2.2 to 80.0 cN•mm for moment component, respectively. The relative difference of the simulation results ranged from 2.5% to 11.0% for force component, and ranged from 0.6% to 14.7% for moment component, respectively. CONCLUSIONS: The developed orthodontic force simulation method based on virtual bracket displacement can be used to simulate orthodontic force provided by the archwire applied to full dentition.

A Method for Tooth Model Reconstruction Based on Integration of Multimodal Images.

A complete digital tooth model is needed for computer-aided orthodontic treatment. However, current methods mainly use computed tomography (CT) images to reconstruct the tooth model which may require multiple CT scans during orthodontic progress, and the reconstructed model is also inaccurate in crown area. This study developed a tooth model reconstruction method based on integration of CT images and laser scan images to overcome these disadvantages. In the method, crown models and complete tooth models are first reconstructed, respectively, from laser scan images and CT images. Then, crown models from laser scan images and tooth models from CT images are registered. Finally, the crown from laser scan images and root from CT images were fused to obtain a new tooth model. Experimental results verified that the developed method is effective to generate the complete tooth model by integrating CT images and laser scan images. Using the proposed method, the reconstructed models provide more accurate crown than CT images, and it is feasible to obtain complete tooth models at any stage of orthodontic treatment by using one CT scan at the pretreatment stage and one laser scan at that stage to avoid multiple CT scans.

Tooth and Alveolar Bone Segmentation From Dental Computed Tomography Images.

Three-dimensional (3D) models of tooth-alveolar bone complex are needed in treatment planning and simulation for computer-aided orthodontics. Tooth and alveolar bone segmentation from computed tomography (CT) images is a fundamental step in reconstructing their models. Due to less application of alveolar bone in conventional orthodontic treatment which may cause undesired side effects, the previous studies mainly focused on tooth segmentation and reconstruction, and did not consider the alveolar bone. In this study, we proposed a method to implement both tooth and alveolar bone segmentation from dental CT images for reconstructing their 3D models. First, the proposed method extracted the connected region of tooth and alveolar bone from CT images using a global convex level set model. Then, individual tooth and alveolar bone are separated from the connected region based on Radon transform and a local level set model. The experimental results showed that the proposed method could successfully complete both the tooth and alveolar bone segmentation from CT images, and outperformed the state of the art tooth segmentation methods in terms of accuracy. This suggests that the proposed method can be used in reconstructing the 3D models of tooth-alveolar bone complex for precise treatment.

Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth.

BACKGROUND AND OBJECTIVE: Tooth segmentation from computed tomography (CT) images is a fundamental step in generating the three-dimensional models of tooth for computer-aided orthodontic treatment. Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth is especially challenging, and no method has been reported previously. This study aimed to develop a method for individual tooth segmentation from these images. METHODS: Tooth contours of maxilla and mandible are first segmented from the volumetric CT images slice-by-slice. For each slice, a line is extracted using the Radon transform to separate neighboring teeth, and each tooth contour is then segmented by a level set model from the corresponding side of the line. Then, each maxillary tooth whose contours overlap with that of mandible ones is detected, and a mesh model is reconstructed from all the contours of these maxillary and mandible teeth with contour overlap. The reconstructed mesh model is segmented using threshold and fast marching watershed method to separate the touched maxillary and mandible teeth. Finally, the separated tooth models are restored to fill the holes to obtain complete tooth models. The proposed method was tested on CT images of ten subjects scanned with natural contacts of maxillary and mandible teeth. RESULTS: For all the tested images, individual tooth regions are extracted successfully, and the segmentation accuracy and efficiency of the proposed method is promising. CONCLUSIONS: The proposed method is effective to segment individual tooth from CT images scanned with contacts of maxillary and mandible teeth.

[Three-dimensional tooth model reconstruction based on fusion of dental computed tomography images and laser-scanned images].

Complete three-dimensional(3D) tooth model provides essential information to assist orthodontists for diagnosis and treatment planning. Currently, 3D tooth model is mainly obtained by segmentation and reconstruction from dental computed tomography(CT) images. However, the accuracy of 3D tooth model reconstructed from dental CT images is low and not applicable for invisalign design. And another serious problem also occurs, i.e. frequentative dental CT scan during different intervals of orthodontic treatment often leads to radiation to the patients. Hence, this paper proposed a method to reconstruct tooth model based on fusion of dental CT images and laser-scanned images. A complete3 D tooth model was reconstructed with the registration and fusion between the root reconstructed from dental CT images and the crown reconstructed from laser-scanned images. The crown of the complete 3D tooth model reconstructed with the proposed method has higher accuracy. Moreover, in order to reconstruct complete 3D tooth model of each orthodontic treatment interval, only one pre-treatment CT scan is needed and in the orthodontic treatment process only the laser-scan is required. Therefore, radiation to the patients can be reduced significantly.

A motion sensing-based framework for robotic manipulation.

To data, outside of the controlled environments, robots normally perform manipulation tasks operating with human. This pattern requires the robot operators with high technical skills training for varied teach-pendant operating system. Motion sensing technology, which enables human-machine interaction in a novel and natural interface using gestures, has crucially inspired us to adopt this user-friendly and straightforward operation mode on robotic manipulation. Thus, in this paper, we presented a motion sensing-based framework for robotic manipulation, which recognizes gesture commands captured from motion sensing input device and drives the action of robots. For compatibility, a general hardware interface layer was also developed in the framework. Simulation and physical experiments have been conducted for preliminary validation. The results have shown that the proposed framework is an effective approach for general robotic manipulation with motion sensing control.

Automatic Multiple-Needle Surgical Planning of Robotic-Assisted Microwave Coagulation in Large Liver Tumor Therapy.

The "robotic-assisted liver tumor coagulation therapy" (RALTCT) system is a promising candidate for large liver tumor treatment in terms of accuracy and speed. A prerequisite for effective therapy is accurate surgical planning. However, it is difficult for the surgeon to perform surgical planning manually due to the difficulties associated with robot-assisted large liver tumor therapy. These main difficulties include the following aspects: (1) multiple needles are needed to destroy the entire tumor, (2) the insertion trajectories of the needles should avoid the ribs, blood vessels, and other tissues and organs in the abdominal cavity, (3) the placement of multiple needles should avoid interference with each other, (4) an inserted needle will cause some deformation of liver, which will result in changes in subsequently inserted needles' operating environment, and (5) the multiple needle-insertion trajectories should be consistent with the needle-driven robot's movement characteristics. Thus, an effective multiple-needle surgical planning procedure is needed. To overcome these problems, we present an automatic multiple-needle surgical planning of optimal insertion trajectories to the targets, based on a mathematical description of all relevant structure surfaces. The method determines the analytical expression of boundaries of every needle "collision-free reachable workspace" (CFRW), which are the feasible insertion zones based on several constraints. Then, the optimal needle insertion trajectory within the optimization criteria will be chosen in the needle CFRW automatically. Also, the results can be visualized with our navigation system. In the simulation experiment, three needle-insertion trajectories were obtained successfully. In the in vitro experiment, the robot successfully achieved insertion of multiple needles. The proposed automatic multiple-needle surgical planning can improve the efficiency and safety of robot-assisted large liver tumor therapy, significantly reduce the surgeon's workload, and is especially helpful for an inexperienced surgeon. The methodology should be easy to adapt in other body parts.

Hounsfield unit change in root and alveolar bone during canine retraction.

INTRODUCTION: The objective of this study was to determine the Hounsfield unit (HU) changes in the alveolar bone and root surfaces during controlled canine retractions. METHODS: Eighteen maxillary canine retraction patients were selected for this split-mouth design clinical trial. The canines in each patient were randomly assigned to receive either translation or controlled tipping treatment. Pretreatment and posttreatment cone-beam computed tomography scans of each patient were used to determine tooth movement direction and HU changes. The alveolar bone and root surface were divided into 108 divisions, respectively. The HUs in each division were measured. Mixed-model analysis of variance was applied to test the HU change distribution at the P <0.05 significance level. RESULTS: The HU changes varied with the directions relative to the canine movement. The HU reductions occurred at the root surfaces. Larger reductions occurred in the divisions that were perpendicular to the moving direction. However, HUs decreased in the alveolar bone in the moving direction. The highest HU reduction was at the coronal level. CONCLUSIONS: HU reduction occurs on the root surface in the direction perpendicular to tooth movement and in the alveolar bone in the direction of tooth movement when a canine is retracted.

Toward accurate tooth segmentation from computed tomography images using a hybrid level set model.

PURPOSE: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. METHODS: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm(3)) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. RESULTS: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm(3), 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm(3), 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0.28 ± 0.03 mm, and 1.06 ± 0.40 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the premolar were 37.95 ± 10.13 mm(3), 92.45 ± 2.29%, 0.29 ± 0.06 mm, 0.33 ± 0.10 mm, and 1.28 ± 0.72 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the molar were 52.38 ± 17.27 mm(3), 94.12 ± 1.38%, 0.30 ± 0.08 mm, 0.35 ± 0.17 mm, and 1.52 ± 0.75 mm, respectively. The computation time of the proposed method for segmenting CBCT images of one subject was 7.25 ± 0.73 min. Compared with two other methods, the proposed method achieves significant improvement in terms of accuracy. CONCLUSIONS: The presented tooth segmentation method can be used to segment tooth contours from CT images accurately and efficiently.