Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble.

Background: Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. Method: The first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca. Results: The experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. Conclusions: AI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.

Enhancing teeth segmentation using multifusion deep neural net in panoramic X-ray images.

BACKGROUND: Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is required. OBJECTIVE: This study aims to develop and test a novel multi-fusion deep neural net consisting of encoder-decoder architecture for automatic and accurate teeth region segmentation from panoramic X-ray images. METHODS: The encoder has two different streams based on CNN which include the conventional CNN stream and the Atrous net stream. Next, the fusion of features from these streams is done at each stage to encode the contextual rich information of teeth. A dual-type skip connection is then added between the encoder and decoder to minimise semantic information gaps. Last, the decoder comprises deconvolutional layers for reconstructing the segmented teeth map. RESULTS: The assessment of the proposed model is performed on two different dental datasets consisting of 1,500 and 1,000 panoramic X-ray images, respectively. The new model yields accuracy of 97.0% and 97.7%, intersection over union (IoU) score of 91.1% and 90.2%, and dice coefficient score (DCS) of 92.4% and 90.7% for datasets 1 and 2, respectively. CONCLUSION: Applying the proposed model to two datasets outperforms the recent state-of-the-art deep models with a relatively smaller number of parameters and higher accuracy, which demonstrates the potential of the new model to help dentists more accurately and efficiently diagnose dental diseases in future clinical practice.

Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs.

Teeth segmentation plays a pivotal role in dentistry by facilitating accurate diagnoses and aiding the development of effective treatment plans. While traditional methods have primarily focused on teeth segmentation, they often fail to consider the broader oral tissue context. This paper proposes a panoptic-segmentation-based method that combines the results of instance segmentation with semantic segmentation of the background. Particularly, we introduce a novel architecture for instance teeth segmentation that leverages a dual-path transformer-based network, integrated with a panoptic quality (PQ) loss function. The model directly predicts masks and their corresponding classes, with the PQ loss function streamlining the training process. Our proposed architecture features a dual-path transformer block that facilitates bi-directional communication between the pixel path CNN and the memory path. It also contains a stacked decoder block that aggregates multi-scale features across different decoding resolutions. The transformer block integrates pixel-to-memory feedback attention, pixel-to-pixel self-attention, and memory-to-pixel and memory-to-memory self-attention mechanisms. The output heads process features to predict mask classes, while the final mask is obtained by multiplying memory path and pixel path features. When applied to the UFBA-UESC Dental Image dataset, our model exhibits a substantial improvement in segmentation performance, surpassing existing state-of-the-art techniques in terms of performance and robustness. Our research signifies an essential step forward in teeth segmentation and contributes to a deeper understanding of oral structures.

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

Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods.

Tooth and pulp chamber automatic segmentation with artificial intelligence network and morphometry method in cone-beam CT / Segmentación automática de cámaras dentales y pulpares con red de inteligencia artificial y método de morfometría en TC de haz cónico

SUMMARY: This study aims to extract teeth and alveolar bone structures in CBCT images automatically, which is a key step in CBCT image analysis in the field of stomatology. In this study, semantic segmentation was used for automatic segmentation. Five marked classes of CBCT images were input for U-net neural network training. Tooth hard tissue (including enamel, dentin, and cementum), dental pulp cavity, cortical bone, cancellous bone, and other tissues were marked manually in each class. The output data were from different regions of interest. The network configuration and training parameters were optimized and adjusted according to the prediction effect. This method can be used to segment teeth and peripheral bone structures using CBCT. The time of the automatic segmentation process for each CBCT was less than 13 min. The Dice of the evaluation reference image was 98 %. The U-net model combined with the watershed method can effectively segment the teeth, pulp cavity, and cortical bone in CBCT images. It can provide morphological information for clinical treatment.

RESUMEN: El objetivo del presente estudio fue extraer estructuras dentarias y óseas alveolares desde imágenes CBCT automáticamente, lo cual es un paso clave en el análisis de imágenes CBCT en el campo de la estomatología. En este estudio, se utilizó la segmentación de tipo emántica para la segmentación automática. Se ingresaron cinco clases de imágenes CBCT marcadas, para el entrenamiento de la red neuronal U-net. El tejido duro del diente (incluidos esmalte, dentina y cemento), la cavidad de la pulpa dentaria, hueso cortical, hueso esponjoso y otros tejidos se marcaron manualmente en cada clase. Los datos se obtuvieron de diferentes regiones de interés. La configuración de la red y los parámetros de entrenamiento se optimizaron y ajustaron de acuerdo con un análisis predictivo. Este método se puede utilizar para segmentar dientes y estructuras óseas periféricas mediante CBCT. El tiempo del proceso de segmentación automática para cada CBCT fue menor a 13 min. El "Dice" de evaluación de la imagen de referencia fue de 98 %. El modelo U-net combinado con el método "watershed"puede segmentar eficazmente los dientes, la cavidad pulpar y el hueso cortical en imágenes CBCT. Puede proporcionar información morfológica para el tratamiento clínico.

An Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs.

Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20-50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local communities and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The Macro F1-score and accuracy of the periodontitis prediction task in our method reach 0.894 and 0.896, respectively, on Suzhou data set, and 0.820 and 0.824, respectively on Zhongshan data set. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well.

3-dimensional analysis of regenerative endodontic treatment outcome.

INTRODUCTION: A growing body of evidence supports the regeneration potential of dental tissues after regenerative endodontic treatment (RET). Nevertheless, a standard method for the evaluation of RET outcome is lacking. The aim of this study was to develop a standardized quantitative method for RET outcome analysis based on cone-beam computed tomographic (CBCT) volumetric measurements. METHODS: Five human teeth embedded in mandibular bone samples were scanned using both an Accuitomo 170 CBCT machine (Morita, Kyoto, Japan) and a SkyScan 1174 micro-computed tomographic (µCT) system (SkyScan, Antwerp, Belgium). For subsequent clinical application, clinical data and low-dose CBCT scans (preoperatively and follow-up) from 5 immature permanent teeth treated with RET were retrieved. In vitro and clinical 3-dimensional image data sets were imported into a dedicated software tool. Two segmentation steps were applied to extract the teeth of interest from the surrounding tissue (livewire) and to separate tooth hard tissue and root canal space (level set methods). In vitro and clinical volumetric measurements were assessed separately for differences using Wilcoxon matched pairs test. Pearson correlation analysis and Bland-Altman plots were used to evaluate the relation and agreement between the segmented CBCT and µCT volumes. RESULTS: The results showed no statistical differences and strong agreement between CBCT and µCT volumetric measurements. Volumetric comparison of the root hard tissue showed significant hard tissue formation. (The mean volume of newly formed hard tissue was 27.9 [±10.5] mm(3) [P < .05]). CONCLUSIONS: Analysis of 3-dimensional data for teeth treated with RET offers valuable insights into the treatment outcome and patterns of hard tissue formation.

Teeth segmentation of dental periapical radiographs based on local singularity analysis.

Teeth segmentation for periapical raidographs is one of the most critical tasks for effective periapical lesion or periodontitis detection, as both types of anomalies usually occur around tooth boundaries and dental radiographs are often subject to noise, low contrast, and uneven illumination. In this paper, we propose an effective scheme to segment each tooth in periapical radiographs. The method consists of four stages: image enhancement using adaptive power law transformation, local singularity analysis using Hölder exponent, tooth recognition using Otsu's thresholding and connected component analysis, and tooth delineation using snake boundary tracking and morphological operations. Experimental results of 28 periapical radiographs containing 106 teeth in total and 75 useful for dental examination demonstrate that 105 teeth are successfully isolated and segmented, and the overall mean segmentation accuracy of all 75 useful teeth in terms of (TP, FP) is (0.8959, 0.0093) with standard deviation (0.0737, 0.0096), respectively.