Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images

Deep learning-based tooth segmentation methods in medical imaging: A review

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview

OBJECTIVES

This article reviews recent advances in computer-aided segmentation methods for oral and maxillofacial surgery and describes the advantages and limitations of these methods. The objective is to provide an invaluable resource for precise therapy and surgical planning in oral and maxillofacial surgery. Study selection, data and sources: This review includes full-text articles and conference proceedings reporting the application of segmentation methods in the field of oral and maxillofacial surgery. The research focuses on three aspects: tooth detection segmentation, mandibular canal segmentation and alveolar bone segmentation. The most commonly used imaging technique is CBCT, followed by conventional CT and Orthopantomography. A systematic electronic database search was performed up to July 2023 (Medline via PubMed, IEEE Xplore, ArXiv, Google Scholar were searched).

RESULTS

These segmentation methods can be mainly divided into two categories: traditional image processing and machine learning (including deep learning). Performance testing on a dataset of images labeled by medical professionals shows that it performs similarly to dentists' annotations, confirming its effectiveness. However, no studies have evaluated its practical application value.

CONCLUSION

Segmentation methods (particularly deep learning methods) have demonstrated unprecedented performance, while inherent challenges remain, including the scarcity and inconsistency of datasets, visible artifacts in images, unbalanced data distribution, and the "black box" nature.

CLINICAL SIGNIFICANCE

Accurate image segmentation is critical for precise treatment and surgical planning in oral and maxillofacial surgery. This review aims to facilitate more accurate and effective surgical treatment planning among dental researchers.

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.

Evaluation of mandibular bone density in bruxers: the value of panoramic radiographs

OBJECTIVES

This study aimed to establish a difference in mandibular bone density between bruxer and non-bruxer patients, based on panoramic radiographs.

METHODS

Panoramic radiographs of bruxer and non-bruxer patients were analyzed with ImageJ^®. Several radiological determinants were studied on the patients' panoramic radiographs: gray values of cancellous bone and cortical bone, and bony exostoses at the mandibular angle.

RESULTS

Thirty-seven bruxers and forty-seven non-bruxers were included in the study. A statistically significant difference (p < 0.05) was noted in the cancellous to cortical bone ratios of bruxers and non-bruxers: the density of cancellous bone was greater in bruxers than in non-bruxers. The number of bony exostoses at the mandibular angle was significantly higher in bruxers (p < 0.05).

CONCLUSIONS

This study obtained radiological determinants of bruxism from panoramic radiographs. Further studies are needed to supplement this preliminary approach, especially via the analysis of three-dimensional imaging to overcome the limitations of panoramic radiography.

A Survey of Dental Caries Segmentation and Detection Techniques

Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found out that methods were grouped according to the type of dental caries (proximal, enamel), type of X-ray images used (extraoral, intraoral), and segmentation method (threshold-based, cluster-based, boundary-based, and region-based). From the works reviewed, the main focus has been found to be on threshold-based segmentation methods. Most of the reviewed papers have preferred the use of intraoral X-ray images over extraoral X-ray images to perform segmentation on dental images of already isolated parts of the teeth. This paper presents an in-depth analysis of recent research in deep learning for dental caries segmentation and detection. It involves discussing the methods and algorithms used in segmenting and detecting dental caries. It also discusses various existing models used and how they compare with each other in terms of system performance and evaluation. We also discuss the limitations of these methods, as well as future perspectives on how to improve their performance.

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Deep learning has become an active research topic in the field of medical image analysis. In particular, for the automatic segmentation of stomatological images, great advances have been made in segmentation performance. In this paper, we systematically reviewed the recent literature on segmentation methods for stomatological images based on deep learning, and their clinical applications. We categorized them into different tasks and analyze their advantages and disadvantages. The main categories that we explored were the data sources, backbone network, and task formulation. We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images. For the backbone network, we distinguished methods based on convolutional neural networks from those based on transformers. We divided task formulations into semantic segmentation tasks and instance segmentation tasks. Toward the end of the paper, we discussed the challenges and provide several directions for further research on the automatic segmentation of stomatological images.

Changing trabecular patterns in panoramic radiographs of Swedish women during 25 years of follow-up

OBJECTIVES

The radiographic trabecular pattern on dental radiographs may be used to predict fractures. The aim of this study was to analyze longitudinal changes in the mandibles of 145 females between 1980 and 2005.

METHODS

Panoramic radiographs were obtained in 1980 and 2005. On 290 radiographs, regions of interest (ROIs) were selected in the ramus, angle and body. In all ROIs, the orientation was measured in 36 directions with the line frequency deviation method. The effects of ageing were analyzed for the fracture and the non-fracture groups separately.

RESULTS

During the follow-up, 61 females suffered fractures of the hip, wrist, spine, leg or arm. The fracture and non-fracture groups displayed dissimilar age changes in each investigated ROI. All significant changes pertained to increasing values of line frequency deviation. With increasing age, the trabecular network in the mandible lost details and the trabeculae became more aligned in their main direction. In the "ramus", the alignment was to the 110-120˚ axis, parallel to the posterior and anterior ramus border. In the "angle", the alignment was to the 135-150˚ axis, parallel to the oblique line, and in the "body" ROI to the 150-175˚ direction, approximately parallel to the occlusal plane and inferior cortex.

CONCLUSION

Most changes were consistent with the notion that the bone aged less severely in the non-fracture group. In the fracture group, the findings indicate that bone loss leads to redistribution of the remaining bone tissue in such a way that the trabeculae are accentuated perpendicular to the principal loading.

IDIOS: An innovative index for evaluating dental imaging-based osteoporosis screening indices

Purpose

The goal of this study was to develop a new index as an objective reference for evaluating current and newly developed indices used for osteoporosis screening based on dental images. Its name; IDIOS, stands for Index of Dental-imaging Indices of Osteoporosis Screening.

Materials and Methods

A comprehensive PubMed search was conducted to retrieve studies on dental imaging-based indices for osteoporosis screening. The results of the eligible studies, along with other relevant criteria, were used to develop IDIOS, which has scores ranging from 0 (0%) to 15 (100%). The indices presented in the studies we included were then evaluated using IDIOS.

Results

The 104 studies that were included utilized 24, 4, and 9 indices derived from panoramic, periapical, and computed tomographic/cone-beam computed tomographic techniques, respectively. The IDIOS scores for these indices ranged from 0 (0%) to 11.75 (78.32%).

Conclusion

IDIOS is a valuable reference index that facilitates the evaluation of other dental imaging-based osteoporosis screening indices. Furthermore, IDIOS can be utilized to evaluate the accuracy of newly developed indices.