Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning

Im, Joon; Kim, Ju-Yeong; Yu, Hyung Seog; Lee, Kee‐Joon; Choi, Sung‐Hwan; Kim, Ji-Hoi; Ahn, Hee-Kap; Yul, Jung

doi:10.1038/s41598-022-13595-2

Cited by 29 publications

(14 citation statements)

References 24 publications

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“…STSNet fully utilizes large-scale unlabeled IOS dental data, achieving an 89.88% mIoU and a 92.84% DSC on a private IOS dental dataset. Im et al 70 used three different methods to segment and compare 30 validated tooth models. The three methods are tooth segmentation based on the DGCNN for deep learning, a landmark-based method with OrthoAnalyzer software, and a method with Autolign software.…”

Section: D Tooth Segmentation Methods Based On Gcnsmentioning

confidence: 99%

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

Chen,

Ma,

et al. 2024

Proc Inst Mech Eng H

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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.

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Section: D Tooth Segmentation Methods Based On Gcnsmentioning

confidence: 99%

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

Chen,

Ma,

et al. 2024

Proc Inst Mech Eng H

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“…Although these changes are not always immediately recognized or associated with AI, they are neither small nor insignificant, especially when viewed in a broader context. AI can be used in a variety of ways to improve dental care and dentistry, such as segmenting and identifying teeth ( 89 , 90 ), planning of dental implants treatment, identification and classification of dental implant systems ( 91 ), for detection and classification of dental plague ( 92 ), for diagnosing maxillary sinusitis on panoramic radiography ( 93 ), for cephalometric landmarks detection ( 94 ), or for root morphological classification ( 95 ), dental caries detection on periapical and bitewing X-ray images ( 96 ), and many other applications including ( 97 – 105 ):…”

Section: Artificial Intelligence In Dental Medicinementioning

confidence: 99%

Artificial Intelligence in Medicine and Dentistry

Vodanović¹,

Subašić²,

Milošević³

et al. 2023

Acta Stomatol Croat

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Introduction Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. Objective Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. Conclusion The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

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“…They got the IoU score of 80.4% and 7 the Dice coefficient of 89%. Similarly, the study done in [11] assesses the precision and effectiveness of deep learningbased automatic teeth segmentation using a DGCNN-based algorithm. Three different methods were used to compare electronic dental models.…”

Section: Related Workmentioning

confidence: 99%

“…Although some of them focus on other types of medical images [13], [22] and some provide both teeth segmentation as well as disease classification [21], [19]. The studies done in [11], [14] are based on 3D image segmentation, which is not our area of concern at this moment as we are focusing on 2D panoramic x-rays. Some of the research introduced new novel models for segmentation [16], [11], [14] by combining features from U-net variants or by introducing new features.…”

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation on Panoramic X-ray Images with U-net Architectures

Zannah,

Bashar,

Mushfiq

et al. 2023

Preprint

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The field of medical image analysis is in a constant state of evolution, particularly in the challenging tasks of segmenting organs, diseases, and abnormalities. Therefore, in the realm of dental disease diagnosis, image segmentation plays a crucial role in addressing the difficulties faced by dentists worldwide when diagnosing dental diseases with the naked eye. One prominent deep neural network architecture, known as U-net, originally designed for biomedical image segmentation, has seen multiple variations and advancements aimed at improving its performance. However, the lack of comparative studies has made it challenging to assess the effectiveness of these U-net variants in segmenting dental X-ray images. The primary objective of this research is to conduct a comprehensive performance comparison among various U-net architectures for dental image segmentation. Specifically, we examine six U-net architecture variants: Vanilla U-net, Dense U-net, Attention U-net, SE U-net, Residual U-net, and R2 U-net. These variants employ configurations with two and three convolutional layers in both the encoder and decoder blocks. Our evaluation metrics include Accuracy, Dice coefficient, F1 score, and IoU (Intersection over Union). Among these U-net variants, Vanilla U-net, with two convolutional layers, demonstrated the highest level of performance, achieving an Accuracy of 95.56% and an IoU score of 88% on the validation set. Notably, this model also exhibited a shorter processing time compared to the other architectures. Conversely, when employing three convolutional layers, the Dense U-net variant emerged as the top performer, achieving an Accuracy of 95.94% and an IoU score of 89.07% on the validation set. Furthermore, the segmentation process successfully isolates the teeth from the surrounding structures, which holds promise for improving disease detection through the development of automated disease diagnosis models.

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Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning

Cited by 29 publications

References 24 publications

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

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

Artificial Intelligence in Medicine and Dentistry

Semantic Segmentation on Panoramic X-ray Images with U-net Architectures

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