Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images

A platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures

OBJECTIVES

To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.

MATERIALS AND METHODS

175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening. A DCNN algorithm adopting 2.5D structure was trained for automatic segmentation. Automatic measuring algorithms were further developed to evaluate the clinical reliability of the DCNN.

RESULTS

The median Dice Similarity Coefficient (DSC) for the air cavity, mucosa, teeth and maxillary bone segmentation were 0.990, 0.850, 0.961 and 0.953, respectively. The Intra-class Correlation Coefficien (ICC) of all automatic measuring algorithms exceeded 0.975. The 95% confidence interval (95%CI) of all volumetric metric bias were within ± 0.5 cm3, of all 2D metric bias were within ± 1 mm. The DCNN also produced satisfying outcome for notably incomplete MS and edentulous alveolar crest.

CONCLUSIONS

The DCNN provided clinically reliable results. The automatic measuring algorithms could reveal 3D information embedded in CBCT 2D planes on the basis of automatic segmentation.

CLINICAL RELEVANCE

This platform helps dentists to conduct instant 3D reconstruction and automatic measuring of 3D clinical parameters of MS and adjacent structures.

NasalSeg: A Dataset for Automatic Segmentation of Nasal Cavity and Paranasal Sinuses from 3D CT Images

Modern facial surgical planning and therapeutic strategies rely heavily on the precise segmentation of the nasal cavity and paranasal sinuses from computed tomography (CT) images for quantitative analysis. Nevertheless, manual segmentation is labor-intensive and prone to inconsistencies, highlighting the need for automatic segmentation methods. A significant challenge in this field is the lack of publicly available clinical datasets for research. To address this issue, we introduce NagalSeg, the first large-scale, publicly available dataset for nasal cavity and paranasal sinus segmentation. In comparison to existing nasal structure segmentation datasets, which are either private or small-scale, NagalSeg stands out as the first publicly accessible dataset. It provides an order of magnitude more labeled data, consisting of 130 3D CT scans with pixel-wise annotations of five anatomical structures: the left nasal cavity, right nasal cavity, nasopharynx, left maxillary sinus, and right maxillary sinus. The NagalSeg dataset serves as an open-access resource to facilitate the development and evaluation of segmentation algorithms and promote future in-depth research towards the clinical application of artificial intelligence methods.

A transcrestal sinus floor elevation strategy based on a haptic robot system: An in vitro study

OBJECTIVES

To reveal the force profiles recorded by haptic autonomous robotic force feedback during the transcrestal sinus floor elevation (TSFE) process, providing a reference for the surgery strategy during TSFE.

MATERIALS AND METHODS

A total of 42 maxillary sinus models with different angles of the sinus floor (30°, 40°, 50°, 60°, 70°, 80°, and 90°, compared to vertical plane) were 3D printed. Implant site preparation was performed using a robotic system, and the total force (Ft) and axial force along the drill (Fz) during the surgery were recorded by the haptic robotic arm. The actual initial breakthrough point (drill contacting sinus floor) and complete breakthrough point (drill penetrating the sinus floor) were defined visually (the actual IBP and the actual CBP). The theoretical initial breakthrough point (the theoretical IBP) and the theoretical complete breakthrough point (the theoretical CBP) defined by the robot-guided system and the CBCT were determined by real-time force feedback and imaging distance measurement, respectively. The distance from the bottom of the resin model to the actual IBP and the actual CBP was defined as Di and Dt, respectively.

RESULTS

The difference in Fz began to increase significantly at 70°, while the difference in Ft became significant at 60°. When the angle was greater than 70°, there was no significant difference in the discrepancy between the actual and theoretical perforation points. Compared to judging the breakthrough point by CBCT, real-time force feedback TSFE under robotic surgery achieved more accurate initial breakthrough point detection.

CONCLUSIONS

The smaller the angle, the larger the breakthrough force for the drill. The real-time force feedback of haptic robotic system during TSFE could provide reliable reference for dentists. More clinical studies are needed to further validate the application of robotic surgery assisted TSFE.

An overview of artificial intelligence based applications for assisting digital data acquisition and implant planning procedures

OBJECTIVES

To provide an overview of the current artificial intelligence (AI) based applications for assisting digital data acquisition and implant planning procedures.

OVERVIEW

A review of the main AI-based applications integrated into digital data acquisitions technologies (facial scanners (FS), intraoral scanners (IOSs), cone beam computed tomography (CBCT) devices, and jaw trackers) and computer-aided static implant planning programs are provided.

CONCLUSIONS

The main AI-based application integrated in some FS's programs involves the automatic alignment of facial and intraoral scans for virtual patient integration. The AI-based applications integrated into IOSs programs include scan cleaning, assist scanning, and automatic alignment between the implant scan body with its corresponding CAD object while scanning. The more frequently AI-based applications integrated into the programs of CBCT units involve positioning assistant, noise and artifacts reduction, structures identification and segmentation, airway analysis, and alignment of facial, intraoral, and CBCT scans. Some computer-aided static implant planning programs include patient's digital files, identification, labeling, and segmentation of anatomical structures, mandibular nerve tracing, automatic implant placement, and surgical implant guide design.

Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model

BACKGROUND

Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this study is to develop a deep learning-based method to perform maxillary sinus segmentation using cone beam computed tomography (CBCT) images. The proposed segmentation method aims to provide better image guidance to surgeons and specialists by determining the boundaries of the maxillary sinus cavities. In this way, more accurate diagnoses can be made and surgical interventions can be performed more successfully.

METHODS

In the study, axial CBCT images of 100 patients (200 maxillary sinuses) were used. These images were marked to identify the maxillary sinus walls. The marked regions are masked for use in the maxillary sinus segmentation model. U-Net, one of the deep learning methods, was used for segmentation. The training process was carried out for 10 epochs and 100 iterations per epoch. The epoch and iteration numbers in which the model showed maximum success were determined using the early stopping method.

RESULTS

After the segmentation operations performed with the U-Net model trained using CBCT images, both visual and numerical results were obtained. In order to measure the performance of the U-Net model, IoU (Intersection over Union) and F1 Score metrics were used. As a result of the tests of the model, the IoU value was found to be 0.9275 and the F1 Score value was 0.9784.

CONCLUSION

The U-Net model has shown high success in maxillary sinus segmentation. In this way, fast and highly accurate evaluations are possible, saving time by reducing the workload of clinicians and eliminating subjective errors.

Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study

OBJECTIVE

The purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist.

MATERIALS AND METHODS

The diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation.

RESULTS

The differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (P=0.049*). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (p<0.001). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst.

CONCLUSION

AI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.

Emergence of artificial intelligence for automating cone-beam computed tomography-derived maxillary sinus imaging tasks. A systematic review

Cone-beam computed tomography (CBCT) imaging of the maxillary sinus is indispensable for implantologists, offering three-dimensional anatomical visualization, morphological variation detection, and abnormality identification, all critical for diagnostics and treatment planning in digital implant workflows. The following systematic review presented the current evidence pertaining to the use of artificial intelligence (AI) for CBCT-derived maxillary sinus imaging tasks. An electronic search was conducted on PubMed, Web of Science, and Cochrane up until January 2024. Based on the eligibility criteria, 14 articles were included that reported on the use of AI for the automation of CBCT-derived maxillary sinus assessment tasks. The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool was used to evaluate the risk of bias and applicability concerns. The AI models used were designed to automate tasks such as segmentation, classification, and prediction. Most studies related to automated maxillary sinus segmentation demonstrated high performance. In terms of classification tasks, the highest accuracy was observed for diagnosing sinusitis (99.7%), whereas the lowest accuracy was detected for classifying abnormalities such as fungal balls and chronic rhinosinusitis (83.0%). Regarding implant treatment planning, the classification of automated surgical plans for maxillary sinus floor augmentation based on residual bone height showed high accuracy (97%). Additionally, AI demonstrated high performance in predicting gender and sinus volume. In conclusion, although AI shows promising potential in automating maxillary sinus imaging tasks which could be useful for diagnostic and planning tasks in implantology, there is a need for more diverse datasets to improve the generalizability and clinical relevance of AI models. Future studies are suggested to focus on expanding the datasets, making the AI model's source available, and adhering to standardized AI reporting guidelines.

Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images

OBJECTIVES

The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.

METHODS

In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.

RESULTS

F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.

CONCLUSIONS

Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

Deep Learning-Based Multi-Class Segmentation of the Paranasal Sinuses of Sinusitis Patients Based on Computed Tomographic Images

Accurate paranasal sinus segmentation is essential for reducing surgical complications through surgical guidance systems. This study introduces a multiclass Convolutional Neural Network (CNN) segmentation model by comparing four 3D U-Net variations-normal, residual, dense, and residual-dense. Data normalization and training were conducted on a 40-patient test set (20 normal, 20 abnormal) using 5-fold cross-validation. The normal 3D U-Net demonstrated superior performance with an F1 score of 84.29% on the normal test set and 79.32% on the abnormal set, exhibiting higher true positive rates for the sphenoid and maxillary sinus in both sets. Despite effective segmentation in clear sinuses, limitations were observed in mucosal inflammation. Nevertheless, the algorithm's enhanced segmentation of abnormal sinuses suggests potential clinical applications, with ongoing refinements expected for broader utility.

Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network

OBJECTIVES

This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel.

METHODS

PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR).

RESULTS

The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%.

CONCLUSIONS

This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.

Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning

Evaluating the mandibular canal proximity is crucial for planning mandibular third molar extractions. Panoramic radiography is commonly used for radiological examinations before third molar extraction but has limitations in assessing the true contact relationship between the third molars and the mandibular canal. Therefore, the true relationship between the mandibular canal and molars can be determined only through additional cone-beam computed tomography (CBCT) imaging. In this study, we aimed to develop an automatic diagnosis method based on a deep learning model that can determine the true proximity between the mandibular canal and third molars using only panoramic radiographs. A total of 901 third molars shown on panoramic radiographs were examined with CBCT imaging to ascertain whether true proximity existed between the mandibular canal and the third molar by two radiologists (450 molars: true contact, 451 molars: true non-contact). Three deep learning models (RetinaNet, YOLOv3, and EfficientDet) were developed, with performance metrics of accuracy, sensitivity, and specificity. EfficientDet showed the highest performance, with an accuracy of 78.65%, sensitivity of 82.02%, and specificity of 75.28%. The proposed deep learning method can be helpful when clinicians must evaluate the proximity of the mandibular canal and a third molar using only panoramic radiographs without CBCT.

A deep learning-based automatic segmentation of zygomatic bones from cone-beam computed tomography images: A proof of concept

OBJECTIVES

To investigate the efficiency and accuracy of a deep learning-based automatic segmentation method for zygomatic bones from cone-beam computed tomography (CBCT) images.

METHODS

One hundred thirty CBCT scans were included and randomly divided into three subsets (training, validation, and test) in a 6:2:2 ratio. A deep learning-based model was developed, and it included a classification network and a segmentation network, where an edge supervision module was added to increase the attention of the edges of zygomatic bones. Attention maps were generated by the Grad-CAM and Guided Grad-CAM algorithms to improve the interpretability of the model. The performance of the model was then compared with that of four dentists on 10 CBCT scans from the test dataset. A p value <.05 was considered statistically significant.

RESULTS

The accuracy of the classification network was 99.64%. The Dice coefficient (Dice) of the deep learning-based model for the test dataset was 92.34 ± 2.04%, the average surface distance (ASD) was 0.1 ± 0.15 mm, and the 95% Hausdorff distance (HD) was 0.98 ± 0.42 mm. The model required 17.03 seconds on average to segment zygomatic bones, whereas this task took 49.3 minutes for dentists to complete. The Dice score of the model for the 10 CBCT scans was 93.2 ± 1.3%, while that of the dentists was 90.37 ± 3.32%.

CONCLUSIONS

The proposed deep learning-based model could segment zygomatic bones with high accuracy and efficiency compared with those of dentists.

CLINICAL SIGNIFICANCE

The proposed automatic segmentation model for zygomatic bone could generate an accurate 3D model for the preoperative digital planning of zygoma reconstruction, orbital surgery, zygomatic implant surgery, and orthodontics.

CBCT for Diagnostics, Treatment Planning and Monitoring of Sinus Floor Elevation Procedures

Sinus floor elevation (SFE) is a standard surgical technique used to compensate for alveolar bone resorption in the posterior maxilla. Such a surgical procedure requires radiographic imaging pre- and postoperatively for diagnosis, treatment planning, and outcome assessment. Cone beam computed tomography (CBCT) has become a well-established imaging modality in the dentomaxillofacial region. The following narrative review is aimed to provide clinicians with an overview of the role of three-dimensional (3D) CBCT imaging for diagnostics, treatment planning, and postoperative monitoring of SFE procedures. CBCT imaging prior to SFE provides surgeons with a more detailed view of the surgical site, allows for the detection of potential pathologies three-dimensionally, and helps to virtually plan the procedure more precisely while reducing patient morbidity. In addition, it serves as a useful follow-up tool for assessing sinus and bone graft changes. Meanwhile, using CBCT imaging has to be standardized and justified based on the recognized diagnostic imaging guidelines, taking into account both the technical and clinical considerations. Future studies are recommended to incorporate artificial intelligence-based solutions for automating and standardizing the diagnostic and decision-making process in the context of SFE procedures to further improve the standards of patient care.

Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

BACKGROUND

The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs).

METHODS

8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool's database. All teeth, crown-bridge restorations, dental implants, composite-amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique.

RESULTS

The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown-bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively.

CONCLUSIONS

Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.

The Use of CBCT in Evaluating the Health and Pathology of the Maxillary Sinus

The use of cone-beam computed tomography (CBCT) has been increasing in dental practice. This narrative review summarized the relevance and utilizations of CBCT to visualize anatomical structures of the maxillary sinus and common pathologies found in the maxillary sinus. The detection/visualization rate, the location and the morphometric characteristics were described. For sinus anatomy, the reviewed features included the posterior superior alveolar artery, sinus pneumatization, sinus hypoplasia, sinus septa, and primary and accessory sinus ostia. For pathology, the following items were reviewed: membrane thickening associated with periapical lesions/periodontal lesions, mucous retention cyst, and antrolith. The visualization and assessment of the maxillary sinus is very important prior to procedures that take place in close proximity with the sinus floor, such as tooth extraction, implant insertion, and sinus floor elevation. Some sinus pathologies may be associated with odontogenic lesions, such as periapical diseases and periodontal bone loss.