Initial contour generation approach in level set methods for dental image segmentation

Segmentation is challenging process in medical images especially on dental x-ray images. Level set methods have effective result on medical and dental image segmentation. Initial Contour (IC) is the essential step in level set image segmentation methods due to start the efficient process. However, the main issue with IC is how to generate the automatic technique in order to reduce the human interaction and moreover, suitable IC to have accurate result. In this paper a new region-based technique for IC generation, is proposed to overcome this issue. The idea is to generate the most suitable IC since the manual initialization of the level set function surface is a well-known drawback for accurate segmentation which has dependency on selection of IC and wrong selection will affect the result. We have utilized the statistical and morphological information inside and outside the contour to establish a region-based map function. This function is able to find the suitable IC on images to perform by level set methods. Experiments on dental x-ray images demonstrate the robustness of segmentation process using proposed method even on noisy images and with weak boundary. Furthermore, computational cost of segmentation process will be reduced

방사선학적 골 소실량과 치주염 단계의 딥러닝 기반 컴퓨터 보조진단 방법: 다기기 연구

학위논문 (박사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(방사선융합의생명전공), 2021. 2. 이원진.Periodontal diseases, including gingivitis and periodontitis, are some of the most common diseases that humankind suffers from. The decay of alveolar bone in the oral and maxillofacial region is one of the main symptoms of periodontal disease. This leads to alveolar bone loss, tooth loss, edentulism, and masticatory dysfunction, which indirectly affects nutrition. In 2017, the American Academy of Periodontology and the European Federation of Periodontology proposed a new definition and classification criteria for periodontitis based on a staging system. Recently, computer-aided diagnosis (CAD) based on deep learning has been used extensively for solving complex problems in radiology. In my previous study, a deep learning hybrid framework was developed to automatically stage periodontitis on dental panoramic radiographs. This was a hybrid of deep learning architecture for detection and conventional CAD processing to achieve classification. The framework was proposed to automatically quantify the periodontal bone loss and classify periodontitis for each individual tooth into three stages according to the criteria that was proposed at the 2017 World Workshop. In this study, the previously developed framework was improved in order to classify periodontitis into four stages by detecting the number of missing teeth/implants using an additional convolutional neural network (CNN). A multi-device study was performed to verify the generality of the method. A total of 500 panoramic radiographs (400, 50, and 50 images for device 1, device 2, and device 3, respectively) from multiple devices were collected to train the CNN. For a baseline study, three CNNs, which were commonly used for segmentation tasks and the modified CNN from the Mask Region with CNN (R-CNN) were trained and tested to compare the detection accuracy using dental panoramic radiographs that were acquired from multiple devices. In addition, a pre-trained weight derived from the previous study was used as an initial weight to train the CNN to detect the periodontal bone level (PBL), cemento-enamel junction level (CEJL), and teeth/implants to achieve a high training efficiency. The CNN, trained with the multi-device images that had sufficient variability, can produce an accurate detection and segmentation for the input images with various aspects. When detecting the missing teeth on the panoramic radiographs, the values of the precision, recall, F1-score, and mean average precision (AP) were set to 0.88, 0.85, 0.87, and 0.86, respectively, by using CNNv4-tiny. As a result of the qualitative and quantitative evaluation for detecting the PBL, CEJL, and teeth/implants, the Mask R-CNN showed the highest dice similarity coefficients (DSC) of 0.96, 0.92, and 0.94, respectively. Next, the automatically determined stages from the framework were compared to those that were developed by three oral and maxillofacial radiologists with different levels of experience. The mean absolute difference (MAD) between the periodontitis staging that was performed by the automatic method and that by the radiologists was 0.31 overall for all the teeth in the whole jaw. The classification accuracies for the images from the multiple devices were 0.25, 0.34, and 0.35 for device 1, device 2, and device 3, respectively. The overall Pearson correlation coefficient (PCC) values between the developed method and the radiologists’ diagnoses were 0.73, 0.77, and 0.75 for the images from device 1, device 2, and device 3, respectively (p < 0.01). The final intraclass correlation coefficient (ICC) value between the developed method and the radiologists’ diagnoses for all the images was 0.76 (p < 0.01). The overall ICC values between the developed method and the radiologists’ diagnoses were 0.91, 0.94, and 0.93 for the images from device 1, device 2, and device 3, respectively (p < 0.01). The final ICC value between the developed method and the radiologists’ diagnoses for all the images was 0.93 (p < 0.01). In the Passing and Bablok analysis, the slopes were 1.176 (p > 0.05), 1.100 (p > 0.05), and 1.111 (p > 0.05) with the intersections of -0.304, -0.199, and -0.371 for the radiologists with ten, five, and three-years of experience, respectively. For the Bland and Altman analysis, the average of the difference between the mean stages that were classified by the automatic method and those diagnosed by the radiologists with ten-years, five-years, and three-years of experience were 0.007 (95 % confidence interval (CI), -0.060 ~ 0.074), -0.022 (95 % CI, -0.098 ~ 0.053), and -0.198 (95 % CI, -0.291 ~ -0.104), respectively. The developed method for classifying the periodontitis stages that combined the deep learning architecture and conventional CAD approach had a high accuracy, reliability, and generality when automatically diagnosing periodontal bone loss and the staging of periodontitis by the multi-device study. The results demonstrated that when the CNN used the training data sets with increasing variability, the performance also improved in an unseen data set.치주염과 치은염을 포함한 치주질환은 인류가 겪고 있는 가장 흔한 질환 중 하나이다. 구강 및 악안면 부위 치조골의 침하는 치주질환의 주요 증상이며, 이는 골 손실, 치아 손실, 치주염을 유발할 수 있으며, 이를 방치할 경우 저작 기능 장애로 인한 영양실조의 원인이 될 수 있다. 2017년 미국치주학회(American Academy of Periodontology)와 유럽치주학회(European Federation of Periodontology)는 공동 워크샵을 통해 치주염에 대한 새로운 정의와 단계 분류 및 진단에 관련된 기준을 발표하였다. 최근, 딥러닝을 기반으로 한 컴퓨터 보조진단 기술 (Computer-aided Diagnoses, CAD)이 의료방사선영상 분야에서 복잡한 문제를 해결하는 데 광범위하게 사용되고 있다. 선행 연구에서 저자는 파노라마방사선영상에서 치주염을 자동으로 진단하기 위한 딥러닝 하이브리드 프레임워크를 개발하였다. 이는 해부학적 구조물 분할을 위한 딥러닝 신경망 기술과 치주염의 단계 분류를 위한 컴퓨터 보조진단 기술을 융합하여 단일 프레임워크에서 치주염을 자동으로 분류, 진단하는 방법이다. 이를 통해 각 치아에서 방사선적 치조골 소실량을 자동으로 정량화하고, 2017년 워크샵에서 제안된 기준에 따라 치주염을 3단계로 분류하였다. 본 연구에서는 선행 개발된 방법을 개선하여 상실 치아와 식립된 임플란트의 수를 검출, 정량화하여 치주염을 4단계로 분류하는 방법을 개발하였다. 또한 개발된 방법의 일반화 정도를 평가하기 위해 서로 다른 기기를 통해 촬영된 영상을 이용한 다기기 연구를 수행하였다. 3개의 기기를 이용하여 총 500매의 파노라마방사선영상을 수집하여 CNN 학습을 위한 데이터셋을 구축하였다. 수집된 영상 데이터셋을 이용하여, 기존 연구에서 의료영상 분할에 일반적으로 사용되는 3개의 CNN 모델과 Mask R-CNN을 학습시킨 후, 해부학적 구조물 분할 정확도 비교 평가를 실시하였다. 또한 CNN의 높은 학습 효율성 확보와 및 다기기 영상에 대한 추가 학습을 위해 선행 연구에서 도출된 사전 훈련 가중치(pre-trained weight)를 이용한 CNN의 전이학습을 실시하였다. CNNv4-tiny를 이용하여 상실 치아를 검출한 결과, 0.88, 0.85, 0.87, 0.86, 0.85의 precision, recall, F1-score, mAP 정확도를 보였다. 해부학적 구조물 분할 결과, Mask R-CNN을 기반으로 수정된 CNN은 치조골 수준에 대해0.96, 백악법랑경계 수준에 대해 0.92, 치아에 대해 0.94의 분할정확도(DSC)를 보였다. 이어 개발된 방법을 이용하여 학습에 사용되지 않은 30매(기기 별 10매)에서 자동으로 결정된 치주염의 단계와 서로 다른 임상경험을 가진 3명의 영상치의학 전문의가 진단한 단계 간 비교 평가를 수행하였다. 평가 결과, 모든 치아에 대해 자동으로 결정된 치주염 단계와 전문의들이 진단한 단계 간 0.31의 오차(MAD)를 보였다. 또한 기기1, 2, 3의 영상에 대해 각각 0.25, 0.34, 0.35의 오차를 보였다. 개발된 방법을 이용한 결과와 방사선 전문의의 진단 사이의 PCC 값은 기기1, 2, 3의 영상에 대해 각각 0.73, 0.77, 0.75로 계산되었다 (p<0.01). 전체 영상에 대한 최종 ICC 값은 0.76 (p<0.01)로 계산되었다. 또한 개발된 방법과 방사선 전문의의 진단 사이의 ICC 값은 기기1, 2, 3의 영상에 대해 각각 0.91, 0.94, 0.93으로 계산되었다 (p <0.01). 마지막으로 최종 ICC 값은 0.93으로 계산되었다 (p<0.01). Passing 및 Bablok 분석의 경우 회귀직선의 기울기와 x축 절편은 교수, 임상강사, 전공의에 대해 각각 1.176 (p>0.05), 1.100 (p>0.05), 1.111 (p>0.05)와 -0.304, -0.199, -0.371로 나타났다. Bland와 Altman 분석의 경우 자동으로 결정된 영상 별 평균 단계와 영상치의학 전공 치과의사의 진단 결과 간 교수, 임상강사, 전공의에 대해 0.007 (95 % 신뢰 구간 (CI), -0.060 ~ 0.074), 각각 -0.022 (95 % CI, -0.098 ~ 0.053), -0.198 (95 % CI, -0.291 ~ -0.104)로 계산되었다. 결론적으로, 본 논문에서 개발된 딥러닝 하이브리드 프레임워크는 딥러닝 신경망 기술과 컴퓨터 보조 진단 기술을 융합하여 환자의 파노라마 방사선 영상에서 치주염을 4단계로 분류하였다. 본 방법은 높은 해부학적 구조물 및 상실 치아 검출 정확도를 보였으며, 자동으로 결정된 치주염 단계는 임상의의 진단 결과와 높은 일치율과 상관성을 보여주었다. 또한 다기기 연구를 통해 개발된 방법의 높은 정확성과 일반화 정도를 검증하였다.CONTENTS Abstract •••••••••••••••••••••••••••••••••••••••••••••••••••••••••• i Contents •••••••••••••••••••••••••••••••••••••••••••••••••••••••• vi List of figures ••••••••••••••••••••••••••••••••••••••••••••••••• viii List of tables •••••••••••••••••••••••••••••••••••••••••••••••••••• x List of abbreviations ••••••••••••••••••••••••••••••••••••••••• xii Introduction •••••••••••••••••••••••••••••••••••••••••••••••••••• 1 Materials and Methods •••••••••••••••••••••••••••••••••••••••• 5 Overall process for deep learning-based computer-aided diagnosis method ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ 5 Data preparation of dental panoramic radiographs from multiple devices ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ 7 Detection of PBL and CEJL structures and teeth using CNNs ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ 10 Detection of the missing teeth using CNNs ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧14 Staging periodontitis by the conventional CAD method ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧17Evaluation of detection and classification performance ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧20 Results ••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 22 Detection performance for the anatomical structures ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ 22 Detection performance for the missing teeth ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ 26 Classification performance for the periodontitis stages ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ 30 Classification performance of correlations, regressions, and agreements between the periodontitis stages ‧‧‧‧‧‧‧ 36 Discussion ••••••••••••••••••••••••••••••••••••••••••••••••••••• 42 References ••••••••••••••••••••••••••••••••••••••••••••••••••••• 55 Abstract in Korean ••••••••••••••••••••••••••••••••••••••••••• 73Docto

Caries detection in panoramic dental x-ray images

The detection of dentalcaries,in a preliminar stage are of most importance. There is a long history of dental caries. Over a million years ago, hominids such as Australopithecus suffered from cavities. Archaeological evidence shows that tooth decay is an ancient disease dating far into prehistory. Skulls dating from a million years ago through the Neolithic period show signs of caries. The increase of caries during the Neolithic period may be attributed to the increase of plant foods containing carbohydrates. The beginning of rice cultivation in South Asia is also believed to have caused an increase in caries. DentalCaries,alsoknownasdentaldecayortoothdecay,isdefinedasadisease of the hard tissues of the teeth caused by the action of microorganisms, found in plaque,onfermentablecarbohydrates(principallysugars). Attheindividuallevel, dental caries is a preventable disease. Given its dynamic nature the dental caries disease, once established, can be treated or reversed prior to significant cavitation taking place. There three types of dental caries [59], the first type is the Enamel Caries, that is preceded by the formation of a microbial dental plaque. Secondly the Dentinal Caries which begins with the natural spread of the process along the natural spread of great numbers of the dentinal tubules. Thirdly the Pulpal Caries that corresponds to the root caries or root surface caries. Primary diagnosis involves inspection of all visible tooth surfaces using a good light source, dental mirror and explorer. Dental radiographs (X-rays) may show dental caries before it is otherwise visible, particularly caries between the teeth. Large dental caries are often apparent to the naked eye, but smaller lesions can be difficult to identify. Visual and tactile inspection along with radiographs are employed frequently among dentists. At times, caries may be difficult to detect. Bacteriacanpenetratetheenameltoreachdentin,butthentheoutersurfacemaybe at first site intact. These caries, sometimes referred to as "hidden caries", in the preliminary stage X-ray are the only way to detect them, despite of the visual examinationofthetoothshowntheenamelintactorminimallyperforated. Without X-rays wouldn’t be possible to detect these problems until they had become severe and caused serious damage. [...

Geometrical modeling of complete dental shapes by using panoramic X-ray, digital mouth data and anatomical templates

In the field of orthodontic planning, the creation of a complete digital dental model to simulate and predict treatments is of utmost importance. Nowadays, orthodontists use panoramic radiographs (PAN) and dental crown representations obtained by optical scanning. However, these data do not contain any 3D information regarding tooth root geometries. A reliable orthodontic treatment should instead take into account entire geometrical models of dental shapes in order to better predict tooth movements. This paper presents a methodology to create complete 3D patient dental anatomies by combining digital mouth models and panoramic radiographs. The modeling process is based on using crown surfaces, reconstructed by optical scanning, and root geometries, obtained by adapting anatomical CAD templates over patient specific information extracted from radiographic data. The radiographic process is virtually replicated on crown digital geometries through the Discrete Radon Transform (DRT). The resulting virtual PAN image is used to integrate the actual radiographic data and the digital mouth model. This procedure provides the root references on the 3D digital crown models, which guide a shape adjustment of the dental CAD templates. The entire geometrical models are finally created by merging dental crowns, captured by optical scanning, and root geometries, obtained from the CAD templates

CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation

Accurate tooth volume segmentation is a prerequisite for computer-aided dental analysis. Deep learning-based tooth segmentation methods have achieved satisfying performances but require a large quantity of tooth data with ground truth. The dental data publicly available is limited meaning the existing methods can not be reproduced, evaluated and applied in clinical practice. In this paper, we establish a 3D dental CBCT dataset CTooth+, with 22 fully annotated volumes and 146 unlabeled volumes. We further evaluate several state-of-the-art tooth volume segmentation strategies based on fully-supervised learning, semi-supervised learning and active learning, and define the performance principles. This work provides a new benchmark for the tooth volume segmentation task, and the experiment can serve as the baseline for future AI-based dental imaging research and clinical application development

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

AUTOMATIC RECOGNITION OF DENTAL PATHOLOGIES AS PART OF A CLINICAL DECISION SUPPORT PLATFORM

The current work is done within the context of Romanian National Program II (PNII) research project "Application for Using Image Data Mining and 3D Modeling in Dental Screening" (AIMMS). The AIMMS project aims to design a program that can detect anatomical information and possible pathological formations from a collection of digital imaging and communications in medicine (DICOM) images. The main function of the AIMMS platform is to provide the user with the opportunity to use an integrated dental support platform, using image processing techniques and 3D modeling. From the literature review, it can be found that for the detection and classification of teeth and dental pathologies existing studies are in their infancy. Therefore, the work reported in this article makes a scientific contribution in this field. In this article it is presented the relevant literature review and algorithms that were created for detection of dental pathologies in the context of research project AIMMS