Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images

Arora, Saurabh; Tripathy, Santosh Kumar; Gupta, Ruchir; Srivastava, Rajeev

doi:10.1177/09544119231157137

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…Ahn et al [29] reported significant time savings with deep learning models in detecting mesiodens, with 1.5 ± 1.4 seconds for diagnosis compared to 811.8 ± 426.1 seconds for general practitioners. In semantic segmentation, various artificial models have been applied to automated teeth segmentation [32,33], dental caries detection [34,35], detection of third molars and mandibular nerves [36], dental restorations [37], and ectopic eruption of teeth [38]. They demonstrated effective performance with significantly reduced diagnostic time.…”

Section: Discussionmentioning

confidence: 99%

Image segmentation of impacted mesiodens using deep learning

2024

JOCPD

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This study aimed to evaluate the performance of deep learning algorithms for the classification and segmentation of impacted mesiodens in pediatric panoramic radiographs. A total of 850 panoramic radiographs of pediatric patients (aged 3-9 years) was included in this study. The U-Net semantic segmentation algorithm was applied for the detection and segmentation of mesiodens in the upper anterior region. For enhancement of the algorithm, pre-trained ResNet models were applied to the encoding path. The segmentation performance of the algorithm was tested using the Jaccard index and Dice coefficient. The diagnostic accuracy, precision, recall, F1-score and time to diagnosis of the algorithms were compared with those of human expert groups using the test dataset. Cohen's kappa statistics were compared between the model and human groups. The segmentation model exhibited a high Jaccard index and Dice coefficient (>90%). In mesiodens diagnosis, the trained model achieved 91-92% accuracy and a 94-95% F1-score, which were comparable with human expert group results (96%). The diagnostic duration of the deep learning model was 7.5 seconds, which was significantly faster in mesiodens detection compared to human groups. The agreement between the deep learning model and human experts is moderate (Cohen's kappa = 0.767). The proposed deep learning algorithm showed good segmentation performance and approached the performance of human experts in the diagnosis of mesiodens, with a significantly faster diagnosis time.

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Section: Discussionmentioning

confidence: 99%

Image segmentation of impacted mesiodens using deep learning

2024

JOCPD

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“…Arora et al [33] presented an approach for automated teeth segmentation in dental panoramic X-ray images using a multimodal convolutional neural network (CNN) architecture.…”

Section: Discussionmentioning

confidence: 99%

Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network

Rubiu,

Bologna,

Cellina

et al. 2023

Applied Sciences

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Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice.

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“…However, its results still needed to be clarified on the coronoid and mandibular condyles, hence the use of the ensemble, which achieved an excellent result in their research. Similarly, Arora et al [38] used a model based on an encoder-decoder architecture. Its encoder part contained several types of CNN-based models to exploit each network and combine their output to generate a fine-grained contextual feature for teeth segmentation.…”

Section: Other Approachesmentioning

confidence: 99%

“…The segmentation of teeth can be considered a pixel-wise classification problem where the model tries to classify whether the given pixel belongs to the background or teeth class. When dealing with medical image segmentation, some approaches use the standard Cross Entropy (CE) [44,38]. In contrast, others use metric-sensitive, minority-class penalizing losses, or a mixture between metricsensitive and Cross Entropy losses [1, 45,19,20] which have shown significant performance in dealing with hard-to-segment regions.…”

Section: Loss Functionmentioning

confidence: 99%

Automatic Teeth Segmentation Using Attention U-Net

Mahran¹,

Hussein²,

Saber³

2023

Preprint

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Teeth segmentation plays a pivotal role in dental diagnosis, treatment, planning, and the development of computer-aided dental systems. It enables precise identification and analysis of dental structures, aiding in detecting dental abnormalities, measuring tooth dimensions, and assessing oral health conditions. Accurate teeth segmentation also facilitates the automation of dental workflows, leading to improved efficiency and reduced human error. Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches developed or enhanced. While Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation, the U-Net architecture has emerged as a standout performer due to its exceptional segmentation capabilities. This paper presents a proof of concept for the Attention U-Net architecture, as previously used in [1], applied to teeth segmentation. The study demonstrates the superior performance of this network in accurately segmenting teeth using a newly available benchmark dataset called Tufts Dental X-Ray Dataset. When trained and tested on 10-fold cross-validation, the model achieved an average dice coefficient of 95.01%, intersection over union of 90.6%, and pixel accuracy of 98.82%. These scores surpass those of all other networks implemented on the same dataset. By leveraging the Attention U-Net architecture, our research showcases the potential of advanced AI techniques in dental radiography. The findings contribute to the ongoing efforts to develop automated systems to assist dental professionals in their clinical practice.

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Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images

Cited by 11 publications

References 38 publications

Image segmentation of impacted mesiodens using deep learning

Image segmentation of impacted mesiodens using deep learning

Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network

Automatic Teeth Segmentation Using Attention U-Net

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