• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.280-286
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• 2024

In this paper, layered UNet with warmup and dropout tricks was used to segment teeth instantly by using data labeled for each individual tooth and increase performance of the result. The layered UNet proposed before showed very good performance in tooth segmentation without distinguishing tooth number. To do instance segmentation of teeth, we labeled teeth CBCT data according to tooth numbering system which is devised by FDI World Dental Federation notation. Colors for labeled teeth are like AI-Hub teeth dataset. Simulation results show that layered UNet does also segment very well for each tooth distinguishing tooth number by color. Layered UNet model using warmup trick was the best with IoU values of 0.80 and 0.77 for training, validation data. To increase the performance of instance segmentation of teeth, we need more labeled data later. The results of this paper can be used to develop medical software that requires tooth recognition, such as orthodontic treatment, wisdom tooth extraction, and implant surgery.

• 전기학회논문지
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• 제58권6호
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• pp.1207-1214
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• 2009

In this study, we propose a novel color-image segmentation algorithm to discern the teeth region utilizing RG intensity and its relevant RGB histogram features with resolving the variations of its maximum intensity in terms of peaks and valleys. Tooth candidates in a CCD image are first extracted by applying RGB color multi-threshold levels and consequently the successive morphological image operations and a Sobel-mask edge processing are performed to resolve the teeth region and its contour.

• 비파괴검사학회지
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• 제29권3호
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• pp.207-213
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• 2009

This paper reports an effort to develop 3D tooth visualization system from CT sequence images as a part of the non-destructive evaluation suitable for the simulation of endodontics, orthodontics and other dental treatments. We focus on the segmentation and visualization for the individual tooth. In dental CT images teeth are touching the adjacent teeth or surrounded by the alveolar bones with similar intensity. We propose an improved level set method with shape prior to separate a tooth from other teeth as well as the alveolar bones. Reconstructed 3D model of individual tooth based on the segmentation results indicates that our technique is a very conducive tool for tooth visualization, evaluation and diagnosis. Some comparative visualization results validate the non-destructive function of our method.

• 정보처리학회논문지B
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• 제11B권2호
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• pp.133-140
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• 2004

최근 치과 분야의 정보화는 환자자료와 진단영상의 취득과 관리 등을 포함하는 통합 정보화시스템으로써 급속히 발전되었다. 이러한 시스템이 성공하기 위해선 의사가 정확하게 질환을 진단하고 치료하도록 양질의 정보를 제공하며 환자들에게 필요한 고가의 치료를 효과적으로 설득할 수 있는 기능이 확보되어야 한다. 이러한 측면에서 치과분야 시뮬레이션이 가능한 3차원 재구성된 치아모델이 필요하다 치과분야의 치아조작은 대부분 개별 치아 단위로 이루어진다. 따라서 3차원 치아 재구성 시스템은 개변치아의 영역분할과 치아에 맞는 재구성기술이 요구된다. 본 논문에서 적응 최적 임계화를 사용한 치아단위 영역분할 방안과 분할된 경계를 사용한 윤곽선 기반방식의 치아 재구성방안을 제안한다. 즉, 연속된 CT영상에서 개별치아 영역을 정확히 분할하기 위해 슬라이스마다 적응적으로 결정된 최적의 임계치를 사용하여 각 치아를 인접한 이웃 치아와 치조골로부터 분리한다. 분할결과는 3차원 재구성되어 개별 치아를 조작하는 사용자의 입력에 따라 3차원 공간상에서 치아의 이동, 발거 동작을 바탕으로 치과 진료의 시뮬레이션을 가능하게 한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
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• pp.177-180
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• 2000

CT images are sequential images that provide medical doctors helpful information for treatment and surgical operation. It is also widely used for the 3D reconstruction of human bone and organs. In the 3D reconstruction, the quality of the reconstructed 3D model heavily depends on the segmentation results. In this paper, we propose an algorithm suitable for the segmentation of teeth and the maxilofacial bone.

• 한국멀티미디어학회논문지
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• 제17권10호
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• pp.1171-1181
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• 2014

In this paper, we propose improved methods which are image conversion and extraction method of watershed seed using morphological characteristic of teeth on complement image. Conventional tooth segmentation methods are occurred low detection ratio at molar region and over, overlap segmentation owing to specular reflection and morphological feature of molars. Therefore, in order to solve the problems of the conventional methods, we propose the image conversion method and improved extraction method of watershed seed. First, the image conversion method is performed using RGB, HSI space of tooth image for to extract boundary and seed of watershed efficiently. Second, watershed seed is reconstructed using morphological characteristic of teeth. Last, individual tooth segmentation is performed using proposed seed of watershed by watershed algorithm. Therefore, as a result of comparison with marker controlled watershed algorithm and the proposed method, we confirmed higher detection ratio and accuracy than marker controlled watershed algorithm.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.562-565
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• 2009

This paper describes an automatic 3-dimensional (3D) segmentation method for 3D CT (Computed Tomography) images using region growing (RG) and edge detection techniques. Specifically, an augmented RG method in which the contours of regions are extracted by a 3D digital edge detection filter is presented. The feature of this method is the capability of preventing the leakage of regions which is a defect of conventional RG method. Experimental results applied to the extraction of teeth from 3D CT data of jaw bones show that teeth are correctly extracted by the proposed method.

• Imaging Science in Dentistry
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• 제53권4호
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• pp.271-281
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• 2023

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

• 대한의용생체공학회:의공학회지
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• 제44권5호
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• pp.346-353
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• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

• Imaging Science in Dentistry
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• 제52권4호
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.