Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray

Dental caries is one of the most chronic diseases that involves the majority of people at least once during their lifetime. This expensive disease accounts for 5-10% of the healthcare budget in developing countries. Caries lesions appear as the result of dental biofi lm metabolic activity, caused by bacteria (most prominently Streptococcus mutans) feeding on uncleaned sugars and starches in oral cavity. Also known as tooth decay, they are primarily diagnosed by general dentists solely based on clinical assessments. Since in many cases dental problems cannot be detected with simple observations, dental x-ray imaging is introduced as a standard tool for domain experts, i.e. dentists and radiologists, to distinguish dental diseases, such as proximal caries. Among different dental radiography methods, Panoramic or Orthopantomogram (OPG) images are commonly performed as the initial step toward assessment. OPG images are captured with a small dose of radiation and can depict the entire patient dentition in a single image. Dental caries can sometimes be hard to identify by general dentists relying only on their visual inspection using dental radiography. Tooth decays can easily be misinterpreted as shadows due to various reasons, such as low image quality. Besides, OPG images have poor quality and structures are not presented with strong edges due to low contrast, uneven exposure, etc. Thus, disease detection is a very challenging task using Panoramic radiography. With the recent development of Artificial Intelligence (AI) in dentistry, and with the introduction of Convolutional Neural Network (CNN) for image classification, developing medical decision support systems is becoming a topic of interest in both academia and industry. Providing more accurate decision support systems using CNNs to assist dentists can enhance their diagnosis performance, resulting in providing improved dental care assistance for patients. In the following thesis, the first automated teeth extraction system for Panoramic images, using evolutionary algorithms, is proposed. In contrast to other intraoral radiography methods, Panoramic is captured with x-ray film outside the patient mouth. Therefore, Panoramic x-rays contain regions outside of the jaw, which make teeth segmentation extremely difficult. Considering that we solely need an image of each tooth separately to build a caries detection model, segmentation of teeth from the OPG image is essential. Due to the absence of significant pixel intensity difference between different regions in OPG radiography, teeth segmentation becomes very hard to implement. Consequently, an automated system is introduced to get an OPG as input and gives images of single teeth as the output. Since only a few research studies are utilizing similar task for Panoramic radiography, there is room for improvement. A genetic algorithm is applied along with different image processing methods to perform teeth extraction by jaw extraction, jaw separation, and teeth-gap valley detection, respectively. The proposed system is compared to the state-of-the-art in teeth extraction on other image types. After teeth are segmented from each image, a model based on various untrained and pretrained CNN-based architectures is proposed to detect dental caries for each tooth. Autoencoder-based model along with famous CNN architectures are used for feature extraction, followed by capsule networks to perform classification. The dataset of Panoramic x-rays is prepared by the authors, with help from an expert radiologist to provide labels. The proposed model has demonstrated an acceptable detection rate of 86.05%, and an increase in caries detection speed. Considering the challenges of performing such task on low quality OPG images, this work is a step towards developing a fully automated efficient caries detection model to assist domain experts

Classification of dental x-ray images

Forensic dentistry is concerned with identifying people based on their dental records. Forensic specialists have a large number of cases to investigate and hence, it has become important to automate forensic identification systems. The radiographs acquired after a person is deceased are called the Post-mortem (PM) radiographs, and the radiographs acquired while the person is alive are called the Ante-mortem (AM) radiographs. Dental biometrics automatically analyzes dental radiographs to identify the deceased individuals. While, ante mortem (AM) identification is usually possible through comparison of many biometric identifiers, postmortem (PM) identification is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashes and natural disasters such as Tsunami) most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Under such circumstances, the best candidates for postmortem biometric identification are the dental features because of their survivability and diversity.;In my work, I present two different techniques to classify periapical images as maxilla (upper jaw) or mandible (lower jaw) images and we show a third technique to classify dental bitewing images as horizontally flipped/rotated or horizontally un-flipped/un-rotated. In our first technique I present an algorithm to classify whether a given dental periapical image is of a maxilla (upper jaw) or a mandible (lower jaw) using texture analysis of the jaw bone. While the bone analysis method is manual, in our second technique, I propose an automated approach for the identification of dental periapical images using the crown curve detection Algorithm. The third proposed algorithm works in an automated manner for a large number of database comprised of dental bitewing images. Each dental bitewing image in the data base can be classified as a horizontally flipped or un-flipped image in a time efficient manner

파노라마방사선영상에서 딥러닝 신경망을 이용한 치성 낭과 종양의 자동 진단 방법

학위논문 (박사) -- 서울대학교 대학원 : 치의학대학원 치의학과, 2021. 2. 이원진.Objective: The purpose of this study was to automatically diagnose odontogenic cysts and tumors of the jaw on panoramic radiographs using a deep convolutional neural network. A novel framework method of deep convolutional neural network was proposed with data augmentation for detection and classification of the multiple diseases. Methods: A deep convolutional neural network modified from YOLOv3 was developed for detecting and classifying odontogenic cysts and tumors of the jaw. Our dataset of 1,282 panoramic radiographs comprised 350 dentigerous cysts, 302 periapical cysts, 300 odontogenic keratocysts, 230 ameloblastomas, and 100 normal jaw with no disease. In addition, the number of radiographs was augmented 12-fold by flip, rotation, and intensity changes. The Intersection over union threshold value of 0.5 was used to obtain performance for detection and classification. The classification performance of the developed convolutional neural network was evaluated by calculating sensitivity, specificity, accuracy, and AUC (Area under the ROC curve) for diseases of the jaw. Results: The overall classification performance for the diseases improved from 78.2% sensitivity, 93.9% specificity, 91.3% accuracy, and 0.86 AUC using the convolutional neural network with unaugmented dataset to 88.9% sensitivity, 97.2% specificity, 95.6% accuracy, and 0.94 AUC using the convolutional neural network with augmented dataset. Convolutional neural network using augmented dataset had the following sensitivities, specificities, accuracies, and AUC: 91.4%, 99.2%, 97.8%, and 0.96 for dentigerous cysts, 82.8%, 99.2%, 96.2%, and 0.92 for periapical cysts, 98.4%, 92.3%, 94.0%, and 0.97 for odontogenic keratocysts, 71.7%, 100%, 94.3%, and 0.86 for ameloblastomas, and 100.0%, 95.1%, 96.0%, and 0.94 for normal jaw, respectively. Conclusion: The novel framework convolutional neural network method was developed for automatically diagnosing odontogenic cysts and tumors of the jaw on panoramic radiographs using data augmentation. The proposed convolutional neural network model showed high sensitivity, specificity, accuracy, and AUC despite the limited number of panoramic images involved.1. 목 적 구강악안면영역에서 발생하는 낭종 혹은 종양을 조기에 발견하지 못하여 적절한 치료가 이루어지지 못하고 지연되는 경우가 있다. 이러한 문제를 해결하기 위하여 인공신경망을 기반으로 하는 기계학습 기술인 딥러닝신경망(deep convolutional neural network)을 이용하는 컴퓨터 보조진단은 보다 정확하고 빠른 결과를 제공할 수 있다. 따라서 본 연구에서는 파노라마방사선영상에서 딥러닝신경망을 이용하여 구강악안면에서 자주 나타나는 4가지 질환(함치성낭, 치근단당, 치성각화낭, 법랑모세포종)을 자동으로 검출 및 진단하는 딥러닝신경망을 개발하고 그 정확성을 평가하였다. 2. 방 법 본 연구에서는 파노라마방사선영상에서 악골에 발생한 치성 낭과 종양을 검출하고 진단하기 위하여 YoLoV3를 기반으로 한 딥러닝신경망을 구축하였다. 1999년부터 2017년까지 서울대학교치과병원에서 조직병리학적으로 확진된 함치성낭 350례, 치근단낭 302례, 치성각화낭 300례, 법랑모세포종 230례의 환자로부터 획득한 총 1182매 파노라마방사선영상을 분석하였다. 또한 대조군으로 질환이 없는 정상 파노라마방사선영상 100매를 선택하였다. 파노라마방사선영상 데이터는 감마, 보정, 회전, 뒤집기 기법을 통하여 12배 증강되었다. 총 데이터의 60%는 훈련세트, 20%는 검증세트, 20%는 테스트세트로 사용하였다. 개발된 딥러닝신경망은 5배 교차검증(5-fold cross validation)기법을 이용하여 평가하였다. 본 연구에서 개발한 딥러닝신경망의 성능은 정확도(Accuracy), 민감도(sensitivity), 특이도(specificity) 및 ROC분석을 통한 AUC(area under the curve) 지표를 사용하여 측정하였다. 3. 결 과 본 연구에서 개발한 딥러닝신경망은 데이터 증강을 하지 않았을 때 78.2% 민감도, 93.9% 특이도, 91.3% 정확도 및 0.86의 AUC 값을 보였고 데이터 증강을 하였을 때에는 88.9% 민감도, 97.2% 특이도, 95.6% 정확도 및 0.94 AUC의 개선된 성능을 보여주었다. 함치성낭은 91.4% 민감도, 99.2% 특이도, 97.8% 정확도 및 0.96 AUC 값을 보였다. 치근단낭은 82.8% 민감도, 99.2% 특이도, 96.2% 정확도 및 0.92 AUC 값을 나타냈다. 치성각화낭은 98.4% 민감도, 92.3% 특이도, 94.0% 정확도 및 0.97 AUC 결과를 보였다. 법랑모세포종은 71.7% 민감도, 100% 특이도, 94.3% 정확도 및 0.86 AUC의 결과를 보였다. 그리고 정상적인 악골에서는 100% 민감도, 95.1% 특이도, 96.0% 정확도 및 0.97 AUC값을 각각 보였다. 4. 결 론 본 연구에서는 파노라마방사선영상에서 치성 낭과 종양을 자동으로 검출하고 진단하는 딥러닝신경망을 개발하였다. 본 연구는 파노라마방사선영상의 수가 충분하지 않았음에도 불구하고 데이터 증강 기법을 이용하여 우수한 민감도, 특이도 및 정확도 결과를 보였다. 본 연구결과를 통하여 개발된 시스템은 환자의 상기 질환을 조기에 진단하고 적절한 시기에 치료하는데 유용하다.Contents Abstract i Tables v Figure legends vi Introduction １ Materials and Methods ５ Data preparation and augmentation of panoramic radiographs ５ A deep convolutional neural network model for detection and classification of multiple diseases YOLOv3 ９ Evaluation of detection and classification performance of the deep convolutional neural network model １３ Results １５ Discussion ２８ Conclusion ３７ Acknowledgments ３８ References ３９ 요약(국문초록) ４８Docto