Teeth Detection and Dental Problem Classification in Panoramic X-Ray Images using Deep Learning and Image Processing Techniques

Muresan, Mircea Paul; Barbura, Andrei Razvan; Nedevschi, Sergiu

doi:10.1109/iccp51029.2020.9266244

Cited by 54 publications

(28 citation statements)

References 21 publications

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“…The methods for achieving the purpose of identifying the medical condition are directed toward the use of artificial intelligence. One is the example of the usage of artificial intelligence [21] that develop a new CNN model to determine the condition of the three dental caries of the panoramic image. The overall accuracy [22] is up to 86% using CNN for detecting teeth and classifying problems.…”

Section: Introductionmentioning

confidence: 99%

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Lin

Chou

et al. 2021

Sensors

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Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

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Section: Introductionmentioning

confidence: 99%

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Lin

Chou

et al. 2021

Sensors

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“…Segmentation and Classification: Muresan [38] proposed a deep learning-based semantic segmentation technique to classify and segment 15 different classes of specific tooth issues. The proposed solution consists of 5 main stages.…”

Section: Related Workmentioning

confidence: 99%

Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems

Panetta

Rajendran

Ramesh

et al. 2022

IEEE J. Biomed. Health Inform.

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The application of Artificial Intelligence in dental healthcare has a very promising role due to the abundance of imagery and non-imagery-based clinical data. Expert analysis of dental radiographs can provide crucial information for clinical diagnosis and treatment. In recent years, Convolutional Neural Networks have achieved the highest accuracy in various benchmarks, including analyzing dental X-ray images to improve clinical care quality. The Tufts Dental Database, a new X-ray panoramic radiography image dataset, has been presented in this paper. This dataset consists of 1000 panoramic dental radiography images with expert labeling of abnormalities and teeth. The classification of radiography images was performed based on five different levels: anatomical location, peripheral characteristics, radiodensity, effects on the surrounding structure, and the abnormality category. This first-of-its-kind multimodal dataset also includes the radiologist's expertise captured in the form of eye-tracking and think-aloud protocol. The contributions of this work are 1) publicly available dataset that can help researchers to incorporate human expertise into AI and achieve more robust and accurate abnormality detection; 2) a benchmark performance analysis for various state-of-the-art systems for dental radiograph image enhancement and image segmentation using deep learning; 3) an in-depth review of various panoramic dental image datasets, along with segmentation and detection systems. The release of this dataset aims to propel the development of AI-powered automated abnormality detection and classification in dental panoramic radiographs, enhance tooth segmentation algorithms, and the ability to distill the radiologist's expertise into AI.

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“…In Reference 17, an improved network based on U‐net has achieved good results on the task of teeth segmentation. In Reference 18, Mircea Paul Muresan et al developed ERFNet 19 to segment different semantic teeth, and then applied a two‐step labeling algorithm to fine‐tune the segmentation results. However, the study in Reference 18 failed to segment each tooth according to the tooth position.…”

Section: Related Workmentioning

confidence: 99%

“…In Reference 18, Mircea Paul Muresan et al developed ERFNet 19 to segment different semantic teeth, and then applied a two‐step labeling algorithm to fine‐tune the segmentation results. However, the study in Reference 18 failed to segment each tooth according to the tooth position. Hsu et al 20 attached a weak supervision mechanism based on U‐net and target detection to realize the recognition of each tooth position.…”

Section: Related Workmentioning

confidence: 99%

BDU‐net: Toward accurate segmentation of dental image using border guidance and feature map distortion

Zhang

Zhu

Hao

et al. 2022

Int J Imaging Syst Tech

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The importance of instance segmentation for each tooth is increasing in dental disease diagnosis and computer-assisted treatment. However, most existing segmentation methods are mainly concerned with semantic feature extraction, while ignoring complex situations such as blurred boundaries and dislocation of teeth in panoramic radiographs. To address these problems, we propose a dual subnetworks structure based on border guidance and feature map distortion, called BDU-net. Specifically, we embed the Disout method with the capability to distort features into the encoder to obtain multi-scale feature maps with excellent generalization. Then, the distorted feature map is submitted to two subnetworks, each having a feature pyramid structure in a shared manner, including a border subnetwork that adjusts the segmentation boundaries and a region subnetwork that generates the region segmentation results. Experiments conducted on the teeth segmentation dataset show that the proposed BDU-net performs better than the other approaches, especially for the case of teeth missing or dislocated teeth.

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Teeth Detection and Dental Problem Classification in Panoramic X-Ray Images using Deep Learning and Image Processing Techniques

Cited by 54 publications

References 21 publications

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems

BDU‐net: Toward accurate segmentation of dental image using border guidance and feature map distortion

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