Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method

Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis

OBJECTIVES

This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques.

DATA

The data analyzed in this review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC).

SOURCES

PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques.

CONCLUSIONS

AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies.

CLINICAL SIGNIFICANCE

AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice.

Efficient complete denture metal base design via a dental feature-driven segmentation network

BACKGROUND AND OBJECTIVE

Complete denture is a common restorative treatment in dental patients and the design of the core components (major connector and retentive mesh) of complete denture metal base (CDMB) is the basis of successful restoration. However, the automated design process of CDMB has become a challenging task primarily due to the complexity of manual interaction, low personalization, and low design accuracy.

METHODS

To solve the existing problems, we develop a computer-aided Segmentation Network-driven CDMB design framework, called CDMB-SegNet, to automatically generate personalized digital design boundaries for complete dentures of edentulous patients. Specifically, CDMB-SegNet consists of a novel upright-orientation adjustment module (UO-AM), a dental feature-driven segmentation network, and a specific boundary-optimization design module (BO-DM). UO-AM automatically identifies key points for locating spatial attitude of the three-dimensional dental model with arbitrary posture, while BO-DM can result in smoother and more personalized designs for complete denture. In addition, to achieve efficient and accurate feature extraction and segmentation of 3D edentulous models with irregular gingival tissues, the light-weight backbone network is also incorporated into CDMB-SegNet.

RESULTS

Experimental results on a large clinical dataset showed that CDMB-SegNet can achieve superior performance over the state-of-the-art methods. Quantitative evaluation (major connector/retentive mesh) showed improved Accuracy (98.54 ± 0.58 %/97.73 ± 0.92 %) and IoU (87.42 ± 5.48 %/70.42 ± 7.95 %), and reduced Maximum Symmetric Surface Distance (4.54 ± 2.06 mm/4.62 ± 1.68 mm), Average Symmetric Surface Distance (1.45 ± 0.63mm/1.28 ± 0.54 mm), Roughness Rate (6.17 ± 1.40 %/6.80 ± 1.23 %) and Vertices Number (23.22 ± 1.85/43.15 ± 2.72). Moreover, CDMB-SegNet shortened the overall design time to around 4 min, which is one tenth of the comparison methods.

CONCLUSIONS

CDMB-SegNet is the first intelligent neural network for automatic CDMB design driven by oral big data and dental features. The designed CDMB is able to couple with patient's personalized dental anatomical morphology, providing higher clinical applicability compared with the state-of-the-art methods.

Progress of Artificial Intelligence-Driven Solutions for Automated Segmentation of Dental Pulp Space on Cone-Beam Computed Tomography Images. A Systematic Review

INTRODUCTION

Automated segmentation of 3-dimensional pulp space on cone-beam computed tomography images presents a significant opportunity for enhancing diagnosis, treatment planning, and clinical education in endodontics. The aim of this systematic review was to investigate the performance of artificial intelligence-driven automated pulp space segmentation on cone-beam computed tomography images.

METHODS

A comprehensive electronic search was performed using PubMed, Web of Science, and Cochrane databases, up until February 2024. Two independent reviewers participated in the selection of studies, data extraction, and evaluation of the included studies. Any disagreements were resolved by a third reviewer. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the risk of bias.

RESULTS

Thirteen studies that met the eligibility criteria were included. Most studies demonstrated high accuracy in their respective segmentation methods, although there was some variation across different structures (pulp chamber, root canal) and tooth types (single-rooted, multirooted). Automated segmentation showed slightly superior performance for segmenting the pulp chamber compared to the root canal and single-rooted teeth compared to multi-rooted ones. Furthermore, the second mesiobuccal (MB2) canalsegmentation also demonstrated high performance. In terms of time efficiency, the minimum time required for segmentation was 13 seconds.

CONCLUSION

Artificial intelligence-driven models demonstrated outstanding performance in pulp space segmentation. Nevertheless, these findings warrant careful interpretation, and their generalizability is limited due to the potential risk and low evidence level arising from inadequately detailed methodologies and inconsistent assessment techniques. In addition, there is room for further improvement, specifically for root canal segmentation and testing of artificial intelligence performance in artifact-induced images.

Semi or fully automatic tooth segmentation in CBCT images: a review

Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.

MCMC algorithm based on Markov random field in image segmentation

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.

Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement

Objective

This study aimed to accurately segment teeth under complex oral conditions, including complex structural interference among adjacent teeth or malocclusion conditions, such as tooth rotation and displacement caused by dental crowding.

Study design

Cone-beam computed tomography (CBCT) images were obtained from 19 patients with complex oral conditions, and a three-step solution was proposed. This study used a global convex level-set model to extract bony tissue and developed a flexible curve extraction method for separating neighbouring teeth under complex structural interference. In addition, a local level-set model with adaptive edge feature enhancement was proposed to segment individual teeth precisely. This model adaptively enhances edge features based on the structure of the root boundary and accurately distinguishes between the close-contact root and alveolar bone resulting from tooth rotation or displacement.

Results

The experimental results showed that the average Dice similarity coefficient values for incisors, canines, premolars, and molars were 93.30%, 93.47%, 93.24%, and 93.89%, respectively, and the average tooth centroid distances were 0.66, 0.61, 0.87, and 0.80 mm, respectively.

Conclusion

The proposed method can effectively segment teeth without relying on highly precise annotated datasets, yielding satisfactory results even under complex structural interference between adjacent teeth or tooth rotation and displacement caused by dental crowding. It is more robust than the other methods and provides valuable data for further research and clinical practice.

Age estimation based on 3D pulp segmentation of first molars from CBCT images using U-Net

OBJECTIVE

To train a U-Net model to segment the intact pulp cavity of first molars and establish a reliable mathematical model for age estimation.

METHODS

We trained a U-Net model by 20 sets of cone-beam CT images and this model was able to segment the intact pulp cavity of first molars. Utilizing this model, 239 maxillary first molars and 234 mandibular first molars from 142 males and 135 females aged 15-69 years old were segmented and the intact pulp cavity volumes were calculated, followed by logarithmic regression analysis to establish the mathematical model with age as the dependent variable and pulp cavity volume as the independent variable. Another 256 first molars were collected to estimate ages with the established model. Mean absolute error and root mean square error between the actual and the estimated ages were used to assess the precision and accuracy of the model.

RESULTS

The dice similarity coefficient of the U-Net model was 95.6%. The established age estimation model was [Formula: see text] (V is the intact pulp cavity volume of the first molars). The coefficient of determination (R2), mean absolute error and root mean square error were 0.662, 6.72 years, and 8.26 years, respectively.

CONCLUSION

The trained U-Net model can accurately segment pulp cavity of the first molars from three-dimensional cone-beam CT images. The segmented pulp cavity volumes could be used to estimate the human ages with reasonable precision and accuracy.

Denoised encoder-based residual U-net for precise teeth image segmentation and damage prediction on panoramic radiographs

OBJECTIVES

This research focuses on performing teeth segmentation with panoramic radiograph images using a denoised encoder-based residual U-Net model, which enhances segmentation techniques and has the capacity to adapt to predictions with different and new data in the dataset, making the proposed model more robust and assisting in the accurate identification of damages in individual teeth.

METHODS

The effective segmentation starts with pre-processing the Tufts dataset to resize images to avoid computational complexities. Subsequently, the prediction of the defect in teeth is performed with the denoised encoder block in the residual U-Net model, in which a modified identity block is provided in the encoder section for finer segmentation on specific regions in images, and features are identified optimally. The denoised block aids in handling noisy ground truth images effectively.

RESULTS

Proposed module achieved greater values of mean dice and mean IoU with 98.90075 and 98.74147 CONCLUSIONS: Proposed AI enabled model permitted a precise approach to segment the teeth on Tuffs dental dataset in spite of the existence of densed dental filling and the kind of tooth.

CLINICAL SIGNIFICANCE

The proposed model is pivotal for improved dental diagnostics, offering precise identification of dental anomalies. This could revolutionize clinical dental settings by facilitating more accurate treatments and safer examination processes with lower radiation exposure, thus enhancing overall patient care.

Tooth automatic segmentation from CBCT images: a systematic review

OBJECTIVES

To describe the current state of the art regarding technological advances in full-automatic tooth segmentation approaches from 3D cone-beam computed tomography (CBCT) images.

MATERIALS AND METHODS

In March 2023, a search strategy without a timeline setting was carried out through a combination of MeSH terms and free text words pooled through Boolean operators ('AND', 'OR') on the following databases: PubMed, Scopus, Web of Science and IEEE Explore. Randomized and non-randomized controlled trials, cohort, case-control, cross-sectional and retrospective studies in the English language only were included.

RESULTS

The search strategy identified 541 articles, of which 23 have been selected. The most employed segmentation methods were based on deep learning approaches. One article exposed an automatic approach for tooth segmentation based on a watershed algorithm and another article used an improved level set method. Four studies presented classical machine learning and thresholding approaches. The most employed metric for evaluating segmentation performance was the Dice similarity index which ranged from 90 ± 3% to 97.9 ± 1.5%.

CONCLUSIONS

Thresholding appeared not reliable for tooth segmentation from CBCT images, whereas convolutional neural networks (CNNs) have been demonstrated as the most promising approach. CNNs could help overcome tooth segmentation's main limitations from CBCT images related to root anatomy, heavy scattering, immature teeth, metal artifacts and time consumption. New studies with uniform protocols and evaluation metrics with random sampling and blinding for data analysis are encouraged to objectively compare the different deep learning architectures' reliability.

CLINICAL RELEVANCE

Automatic tooth segmentation's best performance has been obtained through CNNs for the different ambits of digital dentistry.

Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning

Accurate and automatic segmentation of individual tooth and root canal from cone-beam computed tomography (CBCT) images is an essential but challenging step for dental surgical planning. In this paper, we propose a novel framework, which consists of two neural networks, DentalNet and PulpNet, for efficient, precise, and fully automatic tooth instance segmentation and root canal segmentation from CBCT images. We first use the proposed DentalNet to achieve tooth instance segmentation and identification. Then, the region of interest (ROI) of the affected tooth is extracted and fed into the PulpNet to obtain precise segmentation of the pulp chamber and the root canal space. These two networks are trained by multi-task feature learning and evaluated on two clinical datasets respectively and achieve superior performances to several comparing methods. In addition, we incorporate our method into an efficient clinical workflow to improve the surgical planning process. In two clinical case studies, our workflow took only 2 min instead of 6 h to obtain the 3D model of tooth and root canal effectively for the surgical planning, resulting in satisfying outcomes in difficult root canal treatments.

Segmentation for Multimodal Brain Tumor Images Using Dual-Tree Complex Wavelet Transform and Deep Reinforcement Learning

Image segmentation is an effective tool for computer-aided medical treatment, to retain the detailed features and edges of the segmented image and improve the segmentation accuracy. Therefore, a segmentation algorithm using deep reinforcement learning (DRL) and dual-tree complex wavelet transform (DTCWT) for multimodal brain tumor images is proposed. First, the bivariate concept in DTCWT is used to determine whether the image noise points belong to the real or imaginary region, and the noise probability is checked after calculation; second, the wavelet coefficients corresponding to the region where the noise is located are selected to transform the noise into normal pixel points by bivariate; then, the conditional probability of occurrence of marker points in the edge and center regions of the image is calculated with the target points, and the initial segmentation of the image is achieved by the known wavelet coefficients; finally, the segmentation framework is constructed using DRL, and the network is trained by loss function to optimize the segmentation results and achieve accurate image segmentation. The experiment was evaluated on BraTS2018 dataset, CQ500 dataset, and a hospital brain tumor dataset. The results show that the algorithm in this paper can effectively remove multimodal brain tumor image noise, and the segmented image has good retention of detail features and edges, and the segmented image has high similarity with the original image. The highest information loss index of the segmentation results is only 0.18, the image boundary error is only about 0.3, and F-value is high, which indicates that the proposed algorithm is accurate and can operate efficiently, and has practical applicability.

A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images

Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.

A neuron image segmentation method based Deep Boltzmann Machine and CV model

Neuron image segmentation has wide applications and important potential values for neuroscience research. Due to the complexity of the submicroscopic structure of neurons cells and the defects of the image quality such as anisotropy, boundary loss and blurriness in electron microscopy-based (EM) imaging, and one faces a challenge in efficient automated segmenting large-scale neuron image 3D datasets, which is an essential prerequisite front-end process for the reconstruction of neuron circuits. Here, a neuron image segmentation method by combining Chan-Vest (CV) model with Deep Boltzmann Machine (DBM) is proposed, and a generative model is used to model and generate the target shape, it take this as a prior information to add global target shape feature constraint to the energy function of CV model, and the shape priori information is fused to assist neuron image segmentation. We applied our method to two 3D-EM datasets from different types of nerve tissue and achieved the best performance consistently across two classical evaluation metrics of neuron segmentation accuracy, namely Variation of Information (VoI) and Adaptive Rand Index (ARI). Experimental results show that the fusion algorithm has high segmentation accuracy, strong robustness, and can characterize the sub-microstructure information of neuron images well.