Assessment of automatic segmentation of teeth using a watershed-based method

Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study

BACKGROUND

Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise.

AIM

The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS).

DESIGN

A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them.

RESULTS

AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively).

CONCLUSION

The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.

Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images

Accurate segmentation of oral surgery-related tissues from cone beam computed tomography (CBCT) images can significantly accelerate treatment planning and improve surgical accuracy. In this paper, we propose a fully automated tissue segmentation system for dental implant surgery. Specifically, we propose an image preprocessing method based on data distribution histograms, which can adaptively process CBCT images with different parameters. Based on this, we use the bone segmentation network to obtain the segmentation results of alveolar bone, teeth, and maxillary sinus. We use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding tasks. The tooth segmentation results can obtain the order information of the dentition. The corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing methods. Its average Dice scores on the tooth, alveolar bone, maxillary sinus, and mandibular canal segmentation tasks were 96.5%, 95.4%, 93.6%, and 94.8%, respectively. These results demonstrate that it can accelerate the development of digital dentistry.

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.

Morphological study of pulp cavity anatomy of canine teeth in domestic cats using micro-computed tomography

An understanding of the pulp cavity anatomy of individual teeth is essential for success during endodontic therapy. The objective of this study was to document pulp cavity anatomy and summarize numerical data of maxillary and mandibular canine teeth of domestic cats using micro-computed tomography (micro-CT). Thirty-nine canine teeth from eleven domestic cat cadaveric specimens were extracted and prepared for scanning. Segmentation of the pulp cavity was performed using the Avizo (v2022.2) software package. The morphological features of the pulp cavity including overall shape, configuration, presence of apical deltas and lateral canals was recorded. A quantitative analysis was performed on thirty-one teeth to explore associations between pulp cavity volume and length, apical delta length, maximum apical delta foramina number and cusp-to-tip length using a linear mixed model. Correlation between pertinent continuous variables was assessed using a Pearson's correlation test. Most pulp cavities exhibited varying curvature and ranged from a cylindrical configuration in the coronal third to an ovoid configuration in the middle to apical third. A ribbon-like flattened canal was observed in 6/31 teeth (19%). All canine teeth depicted an apical delta with various configurations except for two teeth that showed a single canal exiting at the apex. In 15/31 teeth (48%), the primary root canal within the apical delta could be clearly identified and in 16/31 (52%) the primary root canal was indiscernible. The results showed that the pulp cavities of maxillary canine teeth were significantly larger and longer and the cusp-to-tip length was longer, when compared to mandibular teeth. The apical delta length was negatively correlated to the volume of the pulp cavity. No specimens depicted lateral canals. This study revealed that the anatomy of the canine tooth pulp cavity in cats can vary considerably and should be a consideration when performing thorough debridement, shaping and obturation of the endodontic system.

Mask refinement network for tooth segmentation on panoramic radiographs

OBJECTIVES

Instance-level tooth segmentation extracts abundant localization and shape information from panoramic radiographs (PRs). The aim of this study was to evaluate the performance of a mask refinement network that extracts precise tooth edges.

METHODS

A public dataset which consists of 543 PRs and 16211 labelled teeth was utilized. The structure of a typical Mask Region-based Convolutional Neural Network (Mask RCNN) was used as the baseline. A novel loss function was designed focus on producing accurate mask edges. In addition to our proposed method, 3 existing tooth segmentation methods were also implemented on the dataset for comparative analysis. The average precisions (APs), mean intersection over union (mIoU), and mean Hausdorff distance (mHAU) were exploited to evaluate the performance of the network.

RESULTS

A novel mask refinement region-based convolutional neural network was designed based on Mask RCNN architecture to extract refined masks for individual tooth on PRs. A total of 3311 teeth were correctly detected from 3382 tested teeth in 111 PRs. The AP, precision, and recall were 0.686, 0.979, and 0.952, respectively. Moreover, the mIoU and mHAU achieved 0.941 and 9.7, respectively, which are significantly better than the other existing segmentation methods.

CONCLUSIONS

This study proposed an efficient deep learning algorithm for accurately extracting the mask of any individual tooth from PRs. Precise tooth masks can provide valuable reference for clinical diagnosis and treatment. This algorithm is a fundamental basis for further automated processing applications.

The impact of teeth and dental restorations on gray value distribution in cone-beam computer tomography: a pilot study

PURPOSE

To investigate the influence of teeth and dental restorations on the facial skeleton's gray value distributions in cone-beam computed tomography (CBCT).

METHODS

Gray value selection for the upper and lower jaw segmentation was performed in 40 patients. In total, CBCT data of 20 maxillae and 20 mandibles, ten partial edentulous and ten fully edentulous in each jaw, respectively, were evaluated using two different gray value selection procedures: manual lower threshold selection and automated lower threshold selection. Two sample t tests, linear regression models, linear mixed models, and Pearson's correlation coefficients were computed to evaluate the influence of teeth, dental restorations, and threshold selection procedures on gray value distributions.

RESULTS

Manual threshold selection resulted in significantly different gray values in the fully and partially edentulous mandible. (p = 0.015, difference 123). In automated threshold selection, only tendencies to different gray values in fully edentulous compared to partially edentulous jaws were observed (difference: 58-75). Significantly different gray values were evaluated for threshold selection approaches, independent of the dental situation of the analyzed jaw. No significant correlation between the number of teeth and gray values was assessed, but a trend towards higher gray values in patients with more teeth was noted.

CONCLUSIONS

Standard gray values derived from CT imaging do not apply for threshold-based bone segmentation in CBCT. Teeth influence gray values and segmentation results. Inaccurate bone segmentation may result in ill-fitting surgical guides produced on CBCT data and misinterpreting bone density, which is crucial for selecting surgical protocols. Created with BioRender.com.

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.

An automated landmark method to describe geometric changes in the human mandible during growth

OBJECTIVE

The principal aim of this study was to assess an automatic landmarking approach to human mandibles based on the atlas method. The secondary aim was to identify the areas of greatest variation in the mandibles of middle-aged to older adults.

DESIGN

Our sample consisted of 160 mandibles from computed tomography scans of 80 men and 80 women aged between 40 and 79 years. Eleven anatomical landmarks were placed manually on mandibles. The automated landmarking through point cloud alignment and correspondence (ALPACA) method implemented in 3D Slicer was used to automatically place landmarks to all meshes. Euclidean distances, normalized centroid size, and Procrustes ANOVA were calculated for both methods. A pseudo-landmarks approach was followed using ALPACA to identify areas of changes among our sample.

RESULTS

The ALPACA method showed significant differences in Euclidean distances for all landmarks compared to the manual method. A mean Euclidean distance of 1.7 mm was found for the ALPACA method and 0.99 mm for the manual method. Both methods found that sex, age, and size had a significant effect on mandibular shape. The greatest variations were observed in the condyle, ramus, and symphysis regions.

CONCLUSION

The results obtained using the ALPACA method are acceptable and promising. This approach can automatically place landmarks with an average accuracy of less than 2 mm, which may be sufficient in most anthropometric analyses. In the light of our results, however, odontological application such as occlusal analysis is not recommended.

Quantitative and qualitative comparisons of pulp cavity volumes produced by cone beam computed tomography and micro-computed tomography through semiautomatic segmentation: An ex vivo investigation

OBJECTIVE

The aim of this study was to measure the volume and visually assess 3-dimensional (3D) virtual models of pulp cavities obtained through semiautomatic segmentation on images from 6 cone beam computed tomography (CBCT) units compared with the reference standard of micro-CT.

STUDY DESIGN

Fifteen mandibular premolar teeth were scanned with 6 CBCT units: Prexion 3D Elite, i-CAT Next Generation, NewTom 5G, Cranex 3D, 3Shape X1, and Orthophos SL 3D, using the smallest available field of view and highest resolution settings. Pulp cavity volumes were quantitatively assessed by 2 calibrated examiners. The volumes from each CBCT unit were compared with micro-CT. Qualitative assessment of the 3D reconstructions was also performed. Repeated-measures analysis of variance and the Friedman test compared the CBCT reconstructions to micro-CT. Intra- and interexaminer agreements were calculated with the intraclass correlation coefficient and kappa statistic.

RESULTS

The CBCT-based volumes were all significantly larger than micro-CT (P ≤ .0061). Prexion, X1, and Orthophos provided the segmentations that most closely resembled the reference standard. Intra- and interexaminer agreements ranged from good to excellent for quantitative measurements. Interexaminer agreement for qualitative evaluation was substantial.

CONCLUSIONS

Semiautomatic segmentation of CBCT images is a feasible method to produce virtual 3D models of the pulp cavity. Prexion, X1, and Orthophos were the CBCT units that resulted in 3D reconstructions most similar to the reference standard.

Improved Watershed Algorithm-Based Microscopic Images Combined with Meibomian Gland Microprobe in the Treatment of Demodectic Blepharitis

The objective of the study was to explore microscopic images under a watershed segmentation algorithm combined with meibomian gland microprobe in the treatment of demodectic blepharitis. For segmenting the connected target objects in the image, the watershed algorithm was utilized first to obtain the target region in the image, and then, the fuzzy C-means (FCM) clustering algorithm was used to cluster the targets. The different grayscale regions in the microscopic images were segmented. 90 patients with demodectic blepharitis-related dry eyes were selected, and they were divided into experimental group 1 (group E1, n = 30), experimental group 2 (group E2, n = 30), and control group (group CG, n = 30). The breakup time (BUT) of the tear film, the subjective score of clinical symptoms, and the number of mites were compared among the three groups before and after treatment. The results showed that after treatment, the indicators of group E1 and group E2 were significantly lower than those before treatment, and the differences were statistically significant (P < 0.05). The treatment effect of group E1 was significantly better than that of the other two groups (P < 0.05). The subjective clinical symptom scores of groups E1, E2, and CG were 13.43 ± 1.41, 13.51 ± 1.41, and 13.64 ± 0.84, respectively, before treatment, and those after treatment were 3.1 ± 1.841, 5.4 ± 0.661, and 13.4 ± 0.841, respectively. The clinical sign scores of the groups E1 and E2 after treatment were remarkably different from those before treatment (P < 0.05). Compared with the scores of clinical signs and clinical symptoms after treatment, those of group E1 showed the largest differences, indicating the best treatment effect. In conclusion, the treatment effect of blepharitis could be promoted with the improved watershed algorithm, and the microscopic images combined with meibomian gland microprobe gave the better effect in the treatment of demodectic blepharitis than the conventional drug heat compress.

Evaluating the Relationship between Mandibular Third Molar and Mandibular Canal with Semiautomatic Segmentation: A Pilot Study on CBCT Datasets

Inferior alveolar nerve injury is the main complication in mandibular third molar surgery. In this context, cone-beam computed tomography (CBCT) has become of crucial importance in evaluating the relationship between mandibular third molar and inferior alveolar nerve. Due to the growing interest in preoperative planning in oral surgery, several post-processing techniques have been implemented to obtain three-dimensional reconstructions of a volume of interest. In the present study, segmentation techniques were retrospectively applied to CBCT images in order to evaluate whether post-processing could offer better visualization of the structures of interest. Forty CBCT examinations performed for inferior third molar impaction were analyzed. Segmentation and volumetric reconstructions were performed. A dataset composed of multiplanar reconstructions for each study case, including segmented images, was submitted for evaluation to two oral surgeons, two general practitioners and four residents in oral surgery. The visualization of root morphology, canal course, and the relationship with mandibular cortical bone on both native CBCT and segmented images were assessed. Inter-rater agreement showed values of intraclass correlation coefficient (ICC) above 0.8 for all the examined parameters. Oral surgeons presented higher ICC values (p < 0.05). Segmented images can improve preoperative evaluation of the third molar and its relationship with the surrounding anatomical structures compared to native CBCT images. Further evaluation is needed to validate these preliminary results.

Micro-Computed Tomography-Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography

INTRODUCTION

This study proposes a novel data pipeline based on micro-computed tomographic (micro-CT) data for training the U-Net network to realize the automatic and accurate segmentation of the pulp cavity and tooth on cone-beam computed tomographic (CBCT) images.

METHODS

We collected CBCT data and micro-CT data of 30 teeth. CBCT data were processed and transformed into small field of view and high-resolution CBCT images of each tooth. Twenty-five sets were randomly assigned to the training set and the remaining 5 sets to the test set. We used 2 data pipelines for U-Net network training: one manually labeled by an endodontic specialist as the control group and one processed from the micro-CT data as the experimental group. The 3-dimensional models constructed using micro-CT data in the test set were taken as the ground truth. The Dice similarity coefficient, precision rate, recall rate, average symmetric surface distance, Hausdorff distance, and morphologic analysis were used for performance evaluation.

RESULTS

The segmentation accuracy of the experimental group measured by the Dice similarity coefficient, precision rate, recall rate, average symmetric surface distance, and Hausdorff distance were 96.20% ± 0.58%, 97.31% ± 0.38%, 95.11% ± 0.97%, 0.09 ± 0.01 mm, and 1.54 ± 0.51 mm in the tooth and 86.75% ± 2.42%, 84.45% ± 7.77%, 89.94% ± 4.56%, 0.08 ± 0.02 mm, and 1.99 ± 0.67 mm in the pulp cavity, respectively, which were better than the control group. Morphologic analysis suggested the segmentation results of the experimental group were better than those of the control group.

CONCLUSIONS

This study proposed an automatic and accurate approach for tooth and pulp cavity segmentation on CBCT images, which can be applied in research and clinical tasks.

Evaluation of curved root canals filled with a new bioceramic sealer: A microcomputed tomographic study using images with different voxel sizes and segmentation methods

The aim of this study was to investigate the filling ability of a new premixed bioceramic sealer in comparison with an epoxy resin-based sealer in curved root canals using different segmentation methods and voxel sizes in micro-CT images. Twelve curved mesial roots of mandibular molars with two separated canals were selected. All root canals were prepared by using HyFlex EDM files size 25/.08 and filled by the single cone technique and Bio-C Sealer or AH Plus (n = 12). The samples were scanned by micro-CT at 5 μm. The images were analyzed at 5, 10, and 20 μm for the volumetric analysis of voids in filling. Visual image segmentation was performed by two examiners, and the automatic segmentation was accomplished for comparison. Radiopacity of the sealers was evaluated by radiographic analysis. Data were submitted to the two-way ANOVA and non-paired t tests at a significance level of 5%. AH Plus had the highest radiopacity (p < .05). Root canals filled with AH Plus or Bio-C had similar low percentage of voids (p > .05). There was no difference interobserver, which had similar results to those obtained with automatic segmentation for all voxel sizes evaluated (p > .05). Bio-C Sealer had appropriate filling ability. Visual and automatic segmentation can be applied to micro-CT images with voxel sizes from 5 to 20 μm to evaluate the filling of sealers with adequate radiopacity. Automatic segmentation should be used as a faster method.

Recent Advances in Cone-beam CT in Oral Medicine

BACKGROUND

The cone-beam computed tomography (CBCT) technology has continuously evolved since its appearance in oral medicine in the early 2000s.

OBJECTIVES

To present recent advances in CBCT in oral medicine: i) selection of recent and consensual evidence-based sources, ii) structured summary of the information based on an iterative framework and iii) compliance with ethical, public health and patient-centered concerns.

MAIN FINDINGS

We will focus on technological advances, such as sensors and reconstruction algorithms used to improve the constant quality of the image and dosimetry. CBCT examination is now performed in almost all disciplines of oral medicine: currently, the main clinical disciplines that use CBCT acquisitions are endodontics and oral surgery, with clearly defined indications. Periodontology and ear, nose and throat medicine are more recent fields of application. For a given application and indication, the smallest possible field of view must be used. One of the major challenges in contemporary healthcare is ensuring that technological developments do not take precedence over admitted standards of care. The entire volume should be reviewed in full, with a systematic approach. All findings are noted in the patient's record and explained to the patient, including incidental findings. This presupposes the person reviewing the images is sufficiently trained to interpret such images, inform the patient and organize the clinical pathway, with referrals to other medical or oral medicine specialties as needed.

CONCLUSION

A close collaboration between dentists, medical physicists, radiologists, radiographers and engineers is critical for all aspects of CBCT technology.

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

OBJECTIVE

To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs.

MATERIALS AND METHODS

In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations.

RESULTS

The system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual.

CONCLUSIONS

The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone.

CLINICAL SIGNIFICANCE

An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.

Ten Years of Micro-CT in Dentistry and Maxillofacial Surgery: A Literature Overview

Micro-computed tomography (micro-CT) is a consolidated imaging technology allowing non-destructive three-dimensional (3D) qualitative and quantitative analysis by the observation of microstructures with high resolution. This paper aims at delivering a structured overview of literature about studies performed using micro-CT in dentistry and maxillofacial surgery (MFS) by analyzing the entire set of articles to portray the state of the art of the last ten years of scientific publications on the topic. It draws the scenario focusing on biomaterials, in vitro and in/ex vivo applications, bone structure analysis, and tissue engineering. It confirms the relevance of the micro-CT analysis for traditional research applications and mainly in dentistry with respect to MFS. Possible developments are discussed in relation to the use of the micro-CT combined with other, traditional, and not, techniques and technologies, as the elaboration of 3D models based on micro-CT images and emerging numerical methods. Micro-CT results contribute effectively with whose ones obtained from other techniques in an integrated multimethod approach and for multidisciplinary studies, opening new possibilities and potential opportunities for the next decades of developments.

How image-processing parameters can influence the assessment of dental materials using micro-CT

Purpose

The aim of this study was to evaluate the influence of voxel size and different post-processing algorithms on the analysis of dental materials using micro-computed tomography (micro-CT).

Materials and Methods

Root-end cavities were prepared in extracted maxillary premolars, filled with mineral trioxide aggregate (MTA), Biodentine, and Intermediate Restorative Material (IRM), and scanned using micro-CT. The volume and porosity of materials were evaluated and compared using voxel sizes of 5, 10, and 20 µm, as well as different software tools (post-processing algorithms). The CTAn or MeVisLab/Materialise 3-matic software package was used to perform volume and morphological analyses, and the CTAn or MeVisLab/Amira software was used to evaluate porosity. Data were analyzed using 1-way ANOVA and the Tukey test (P<0.05).

Results

Using MeVisLab/Materialise 3-matic, a consistent tendency was observed for volume to increase at larger voxel sizes. CTAn showed higher volumes for MTA and IRM at 20 µm. Using CTAn, porosity values decreased as voxel size increased, with statistically significant differences for all materials. MeVisLab/Amira showed a difference for MTA and IRM at 5 µm, and for Biodentine at 20 µm. Significant differences in volume and porosity were observed in all software packages for Biodentine across all voxel sizes.

Conclusion

Some differences in volume and porosity were found according to voxel size, image-processing software, and the radiopacity of the material. Consistent protocols are needed for research evaluating dental materials.

Reliability of orbital volume measurements based on computed tomography segmentation: Validation of different algorithms in orbital trauma patients

PURPOSE

To compare the most common methods of segmentation for evaluation of the bony orbit in orbital trauma patients.

MATERIALS AND METHODS

Computed tomography scans (before and after treatment) from 15 patients with unilateral blowout fractures and who underwent orbital reconstructions were randomly selected for this study. Orbital volume measurements, volume difference measurements, prolapsed soft tissue volumes, and bony defect areas were made using manual, semi-automated, and automated segmentation methods.

RESULTS

Volume difference values between intact and damaged orbits after surgery using the manual mode were 0.5 ± 0.3 cm³, 0.5 ± 0.4 cm³ applying semi-automated method, and 0.76 ± 0.5 cm³, determined by automated segmentation (р = 0.216); the mean volumes (MVs) for prolapsed tissues were 3.0 ± 1.9 cm³, 3.0 ± 2.3 cm³, and 2.8 ± 3.9 cm³ (p = 0.152); and orbital wall defect areas were 4.7 ± 2.8 cm², 4.75 ± 3.1 cm², and 4.9 ± 3.3 cm² (p = 0.674), respectively.

CONCLUSIONS

The analyzed segmentation methods had the same accuracy in evaluation of volume differences between two orbits of the same patient, defect areas, and prolapsed soft tissue volumes but not in absolute values of the orbital volume due to the existing diversity in determination of anterior closing. The automated method is recommended for common clinical cases, as it is less time-consuming with high precision and reproducibility.

Estimation of root inclination of anterior teeth from virtual study models: accuracy of a commercial software

Background

The aim of the study was to assess the accuracy of commercially available software in estimating anterior tooth root inclination from digital impressions of the crowns of the teeth.

Subjects and methods

Following sample size calculation and application of inclusion and exclusion criteria, 55 anterior natural teeth derived from 14 dry human skulls were selected. Impressions were taken and plaster study models were fabricated. Plaster models were scanned using the high-resolution mode of an Ortho Insight 3D laser scanner. The teeth on the digital scans were segmented and virtual roots were predicted and constructed by the Ortho Insight 3D software. The 55 natural teeth were removed from the dry skulls and scanned using the Identica extraoral white-light scanner in order to calculate their actual root angulation. The teeth were scanned twice, once to acquire the crown and the cervical part of the root, and a second time to acquire the remaining part of the root, including the apex. The two scanned segments were joined in software by superimposing them along their common part. The accuracy of the digital models generated by the Ortho Insight 3D scanner in predicting root angulation was assessed by comparing these results to the corresponding measurements of the 55 natural teeth. The long axes of the tooth models obtained from the software prediction and the scanning of the actual teeth were computed and the discrepancy between them was evaluated. The error of the methods was evaluated by repeating the measurements on 14 teeth and showed an acceptable range.

Results

The predicted tooth angulation was found to differ significantly from the actual angulation, both statistically and clinically. The angle between the predicted and actual long axes ranged from 2.0 to 37.6°(average 9.7°; median 7.4°). No statistically significant difference was found between tooth categories.

Conclusions

Further investigations and improvements of the software are needed before it can be considered clinically effective.

Root Canal Anatomy of Maxillary First Premolar by Microscopic Computed Tomography in a Chinese Adolescent Subpopulation

Objectives

To investigate the root morphology and root canal anatomy of maxillary first premolar using microscopic computed tomography (micro-CT).

Methods

324 maxillary first premolars were collected and scanned. The root and canal diameter, canal wall thickness, root taper, and cross-sectional shapes were determined in the single root with 1 canal (SR1C), single root with 2 canals (SR2C), and 2 roots with 2 canals (2R2C) by micro-CT.

Results

The results showed that single-rooted maxillary premolars were more common than other types. The incidence of SR1C, SR2C, and 2R2C reached 25%, 26.39%, and 26.39%, respectively. Root and canal diameters and canal wall thickness were decreased from coronal third to apical foramen. The three parameters and canal taper showed increases from buccal and palatal (BP) to mesiodistal (MD) aspects. The root canal tapers were smallest of the middle third level. The findings showed the different variations in 2R2C teeth. The root canal cross-sectional morphology in maxillary first premolars is complicated, including round, oval, long oval, flat canal, and irregular canal shapes. The distribution varied in different aspects.

Conclusion

Root canal morphology showed a wide variation and complicated structure. The single-rooted teeth were more common in the Chinese adolescent population, and the majority of maxillary first premolars have two canals.

In vivo accuracy of tooth surface reconstruction based on CBCT and dental MRI-A clinical pilot study

OBJECTIVES

Guided implant surgery (GIS) requires alignment of virtual models to reconstructions of three-dimensional imaging. Accurate visualization of the tooth surfaces in the imaging datasets is mandatory. In this prospective clinical study, in vivo tooth surface accuracy was determined for GIS using cone-beam computed tomography (CBCT) and dental magnetic resonance imaging (dMRI).

MATERIALS AND METHODS

CBCT and 3-Tesla dMRI were performed in 22 consecutive patients (mean age: 54.4 ± 15.2 years; mean number of restorations per jaw: 6.7 ± 2.7). Altogether, 92 teeth were included (31 incisor, 29 canines, 20 premolars, and 12 molars). Surfaces were reconstructed semi-automatically and registered to a reference standard (3D scans of stone models made from full-arch polyether impressions). Reliability of both methods was assessed using intraclass correlation coefficients. Accuracy was evaluated using the two one-sided tests procedure with a predefined equivalence margin of ±0.2 mm root mean square (RMS).

RESULTS

Inter- and intrarater reliability of tooth surface reconstruction were comparable for CBCT and dMRI. Geometric deviations were 0.102 ± 0.042 mm RMS for CBCT and 0.261 ± 0.08 mm RMS for dMRI. For a predefined equivalence margin, CBCT and dMRI were statistically equivalent. CBCT, however, was significantly more accurate (p ≤ .0001). For both imaging techniques, accuracy did not differ substantially between different tooth types.

CONCLUSION

Cone-beam computed tomography is an accurate and reliable imaging technique for tooth surfaces in vivo, even in the presence of metal artifacts. In comparison, dMRI in vivo accuracy is lower. Still, it allows for tooth surface reconstruction in satisfactory detail and within acceptable acquisition times.

Marker-based watershed transform method for fully automatic mandibular segmentation from CBCT images

OBJECTIVES:

To propose a reliable and practical method for automatically segmenting the mandible from CBCT images.

METHODS:

The marker-based watershed transform is a region-growing approach that dilates or "floods" predefined markers onto a height map whose ridges denote object boundaries. We applied this method to segment the mandible from the rest of the CBCT image. The height map was generated to enhance the sharp decreases of intensity at the mandible/tissue border and suppress noise by computing the intensity gradient image of the CBCT itself. Two sets of markers, "mandible" and "background" were automatically placed inside and outside the mandible, respectively in a novel image using image registration. The watershed transform flooded the gradient image by dilating the markers simultaneously until colliding at watershed lines, estimating the mandible boundary. CBCT images of 20 adolescent subjects were chosen as test cases. Segmentation accuracy of the proposed method was evaluated by measuring overlap (Dice similarity coefficient) and boundary agreement against a well-accepted interactive segmentation method described in the literature.

RESULTS:

The Dice similarity coefficient was 0.97 ± 0.01 (mean ± SD), indicating almost complete overlap between the automatically and the interactively segmented mandibles. Boundary deviations were predominantly under 1 mm for most of the mandibular surfaces. The errors were mostly from bones around partially erupted wisdom teeth, the condyles and the dental enamels, which had minimal impact on the overall morphology of the mandible.

CONCLUSIONS:

The marker-based watershed transform method produces segmentation accuracy comparable to the well-accepted interactive segmentation approach.