Dental panoramic radiographs are often obtained at dental clinic visits for diagnosis and recording purposes. Automated filing of dental charts can help dentists in reducing their workload and improving diagnostic efficiency. The purpose of this study is to develop a system that prerecords a dental chart by recognizing teeth with their numbers and restoration history on dental panoramic radiographs. The proposed system uses YOLO which detects 16 types of teeth and restoration conditions simultaneously. Based on the detected tooth types, they were further classified into 32 types and combined with the tooth conditions by post-processing. We tested our method on 870 panoramic images obtained at 10 different facilities by 5-fold cross validation. The proposed method obtained 0.99 recall and precision for recognition of 32 tooth types and 0.90 recall and 0.90 precision on determining the tooth condition. It has the potential to assist prefiling the dental charts for efficient dental care.
Purpose: The purpose of our study was to analyze dental panoramic radiographs and contribute to dentists’ diagnosis by automatically extracting the information necessary for reading them. As the initial step, we detected teeth and classified their tooth types in this study.
Approach: We propose single-shot multibox detector (SSD) networks with a side branch for 1-class detection without distinguishing the tooth type and for 16-class detection (i.e., the central incisor, lateral incisor, canine, first premolar, second premolar, first molar, second molar, and third molar, distinguished by the upper and lower jaws). In addition, post-processing was conducted to integrate the results of the two networks and categorize them into 32 classes, differentiating between the left and right teeth. The proposed method was applied to 950 dental panoramic radiographs obtained at multiple facilities, including a university hospital and dental clinics.
Results: The recognition performance of the SSD with a side branch was better than that of the original SSD. In addition, the detection rate was improved by the integration process. As a result, the detection rate was 99.03%, the number of false detections was 0.29 per image, and the classification rate was 96.79% for 32 tooth types.
Conclusions: We propose a method for tooth recognition using object detection and post-processing. The results show the effectiveness of network branching on the recognition performance and the usefulness of post-processing for neural network output.
The purpose of this study is to analyze dental panoramic radiographs for completing dental files to contribute to the diagnosis by dentists. In this study, we recognized 32 tooth types and classified four tooth attributes (tooth, remaining root, pontic, and implant) using 925 dental panoramic radiographs. YOLOv4 and post-processing were used for the recognition of 32 tooth types. As a result, the tooth detection recall was 99.65%, the number of false positives was 0.10 per image, and the 32-type recognition recall was 98.55%. For the classification of the four tooth attributes, two methods were compared. In Method 1, image classification was performed using a clipped image based on the tooth detection result. In Method 2, the labels of tooth attributes were added to the labels of tooth types in object detection. By providing two labels for the same bounding box, we performed multi-label object detection. The accuracy of Method 1 was 0.995 and that of Method 2 was 0.990. Method 2 uses a simple and robust model yet has comparable accuracy as Method 1. In addition, Method 2 did not require additional CNN models. This suggested the usefulness of multi-label detection.
The purpose of this study is to analyze dental panoramic radiographs for completing dental files to contribute to the diagnosis by dentists. As the initial stage, we detected each tooth and classified its tooth type. Since the final goal of this study includes multiple tasks, such as determination of dental conditions and recognition of lesions, we proposed a multitask training based on a Single Shot Multibox Detector (SSD) with a branch to predict the presence or absence of a tooth. The results showed that the proposed model improved the detection rate by 1.0%, the number of false positives per image by 0.03, and the detection rate by tooth type (total number of successfully detected and classified teeth/total number of teeth) by 1.6% compared with the original SSD, suggesting the effectiveness of the multi-task learning in dental panoramic radiographs. In addition, we integrated results of single-class detection without distinguishing the tooth type and 16-class (central incisor, lateral incisor, canine, first premolar, second premolar, first molar, second molar, third molar, distinguished by upper and lower jaws) detection for improving the detection rate and included post-processing for classification of teeth into 32 types and correction of tooth numbering. As a result, the detection rate of 98.8%, 0.33 false positives per image, and classification rate of 92.4% for 32 tooth types were archived.
Dental record plays an important role in dental diagnosis and personal identification. Automatic image preinterpretation can help reducing dentists’ workload and improving diagnostic efficiency. Systematic dental record filing enables effective utilization of accumulated records at dental clinics for forensic identification. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our previous method, two separate networks were employed for detection and classification of teeth. Although detection accuracy was promising, classification performance had a room of improvement. The purpose of this study was to investigate the use of the relation network to utilize information of positional relationship between teeth for the detection and classification. Using the proposed method, both detection and classification performance improved. Especially, the tooth type classification accuracy improved. The proposed method can be useful in automatic filing of the dental chart.
We developed a DentalSCOPE computer program to measure the bone mineral density (BMD) of the alveolar bone. Mineral density measurement of alveolar bone may be useful to predict possible patients who will occur medication-related osteonecrosis of the jaw (MRONJ). Because these osteoporosis medicines affect the mineral density of alveolar bone significantly. The BMD of alveolar bone was compared between dual-energy X-ray absorptiometry (DEXA) and the DentalSCOPE program. A high correlation coefficient was revealed between the DentalSCOPE measurement and the DEXA measurement.
In large disasters, dental record plays an important role in forensic identification. However, filing dental charts for corpses is not an easy task for general dentists. Moreover, it is laborious and time-consuming work in cases of large scale disasters. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our method, individual tooth in CT images are detected and classified into seven tooth types using deep convolutional neural network. We employed the fully convolutional network using AlexNet architecture for detecting each tooth and applied our previous method using regular AlexNet for classifying the detected teeth into 7 tooth types. From 52 CT volumes obtained by two imaging systems, five images each were randomly selected as test data, and the remaining 42 cases were used as training data. The result showed the tooth detection accuracy of 77.4% with the average false detection of 5.8 per image. The result indicates the potential utility of the proposed method for automatic recording of dental information.
KEYWORDS: Bone, Computer aided diagnosis and therapy, Panoramic photography, Radiography, FDA class I medical device development, Computing systems, Dentistry, Minerals, FDA class II medical device development, Neodymium
Findings on dental panoramic radiographs (DPRs) have shown that mandibular cortical index (MCI) based on the morphology of mandibular inferior cortex was significantly correlated with osteoporosis. MCI on DPRs can be categorized into one of three groups and has the high potential for identifying patients with osteoporosis. However, most DPRs are used only for diagnosing dental conditions by dentists in their routine clinical work. Moreover, MCI is not generally quantified but assessed subjectively. In this study, we investigated a computer-aided diagnosis (CAD) system that automatically classifies mandibular cortical bone for detection of osteoporotic patients at early stage. First, an inferior border of mandibular bone was detected by use of an active contour method. Second, regions of interest including the cortical bone are extracted and analyzed for its thickness and roughness. Finally, support vector machine (SVM) differentiate cases into three MCI categories by features including the thickness and roughness. Ninety eight DPRs were used to evaluate our proposed scheme. The number of cases classified to Class I, II, and III by a dental radiologist are 56, 25 and 17 cases, respectively. Experimental result based on the leave-one-out cross-validation evaluation showed that the sensitivities for the classes I, II, and III were 94.6%, 57.7% and 94.1%, respectively. Distribution of the groups in the feature space indicates a possibility of MCI quantification by the proposed method. Therefore, our scheme has a potential in identifying osteoporotic patients at an early stage.
Several studies have reported the presence of carotid artery calcifications (CACs) on dental panoramic radiographs (DPRs) as a possible sign of arteriosclerotic diseases. However, CACs are not easily visible at the common window level for dental examinations, and dentists, in general, are not looking for CACs. Computerized detection of CACs may help dentists in referring patients with a risk of arteriosclerotic diseases to have a detailed examination at a medical clinic. Downside of our previous method was a relatively large number of false positives (FPs). In this study, we attempted to reduce FPs by including an additional feature and selecting effective features for the classifier. A hundred DPRs including 34 cases with calcifications were included. Initial candidates were detected by thresholding the output of top-hat operation. For each candidate, 10 features and a new feature characterizing the relative position of a CAC with reference to the lower mandible edge were determined. After the rule-based FP reduction, candidates were classified into CACs and FPs by a support vector machine. Based on the leave-one-out cross-validation evaluations, an average number of FPs was 3.1 per image at 90.4% sensitivity using seven features selected. Compared to our previous method, the number of FPs was reduced by 38% at the same sensitivity level. The proposed method has a potential in identifying patients with a risk of arteriosclerosis early via general dental examinations.
Findings of dental panoramic radiographs (DPRs) have shown that the mandibular cortical thickness (MCT) was
significantly correlated with osteoporosis. Identifying asymptomatic patients with osteoporosis through dental
examinations may bring a supplemental benefit for the patients. However, most of the DPRs are used for only diagnosing
dental conditions by dentists in their routine clinical work. The aim of this study was to develop a computeraided
diagnosis scheme that automatically measures MCT to assist dentists in screening osteoporosis. First, the inferior
border of mandibular bone was detected by use of an active contour method. Second, the locations of mental foramina
were estimated on the basis of the inferior border of mandibular bone. Finally, MCT was measured on the basis of the
grayscale profile analysis. One hundred DPRs were used to evaluate our proposed scheme. Experimental results showed
that the sensitivity and specificity for identifying osteoporotic patients were 92.6 % and 100 %, respectively. We
conducted multiclinic trials, in which 223 cases have been obtained and processed in about a month. Our scheme
succeeded in detecting all cases of suspected osteoporosis. Therefore, our scheme may have a potential to identify
osteoporotic patients at an early stage.
To identify asymptomatic patients is the challenging task and the essential first step in diagnosis. Findings of dental
panoramic radiographs include not only dental conditions but also radiographic signs that are suggestive of possible
systemic diseases such as osteoporosis, arteriosclerosis, and maxillary sinusitis. Detection of such signs on panoramic
radiographs has a potential to provide supplemental benefits for patients. However, it is not easy for general dental
practitioners to pay careful attention to such signs. We addressed the development of a computer-aided detection (CAD)
system that detects radiographic signs of pathology on panoramic images, and the design of the framework of new
screening pathway by cooperation of dentists and our CAD system. The performance evaluation of our CAD system
showed the sensitivity and specificity in the identification of osteoporotic patients were 92.6 % and 100 %, respectively,
and those of the maxillary sinus abnormality were 89.6 % and 73.6 %, respectively. The detection rate of carotid artery
calcifications that suggests the need for further medical evaluation was approximately 93.6 % with 4.4 false-positives per
image. To validate the utility of the new screening pathway, preliminary clinical trials by using our CAD system were
conducted. To date, 223 panoramic images were processed and 4 asymptomatic patients with suspected osteoporosis, 7
asymptomatic patients with suspected calcifications, and 40 asymptomatic patients with suspected maxillary sinusitis
were detected in our initial trial. It was suggested that our new screening pathway could be useful to identify
asymptomatic patients with systemic diseases.
For gaining a better understanding of bone quality, a great deal of attention has been paid to vertebral geometry in
anatomy. The aim of this study was to design a decision support scheme for vertebral geometries. The proposed scheme
consists of four parts: (1) automated extraction of bone, (2) generation of median plane image of spine, (3) detection of
vertebrae, (4) quantification of vertebral body width, depth, cross-sectional area (CSA), and trabecular bone mineral
density (BMD). The proposed scheme was applied to 10 CT cases and compared with manual tracking performed by an
anatomy expert. Mean differences in the width, depth, CSA, and trabecular BMD were 3.1 mm, 1.4 mm, 88.7 mm2, and
7.3 mg/cm3, respectively. We found moderate or high correlations in vertebral geometry between our scheme and
manual tracking (r > 0.72). In contrast, measurements obtained by using our scheme were slightly smaller than those
acquired from manual tracking. However, the outputs of the proposed scheme in most CT cases were regarded to be
appropriate on the basis of the subjective assessment of an anatomy expert. Therefore, if the appropriate outputs from the
proposed scheme are selected in advance by an anatomy expert, the results can potentially be used for an analysis of
vertebral body geometries.
The multidetector row computed tomography (MDCT) method has the potential to be used for quantitative analysis
of osteoporosis with higher accuracy and precision than that provided by conventional two-dimensional methods. It is
desirable to develop a computer-assisted scheme for analyzing vertebral geometry using body CT images. The aim of
this study was to design a computerized scheme for the localization of vertebral bodies on body CT images. Our new
scheme involves the following steps: (i) Re-formation of CT images on the basis of the center line of the spinal canal to
visually remove the spinal curvature, (ii) use of information on the position of the ribs relative to the vertebral bodies,
(iii) the construction of a simple model on the basis of the contour of the vertebral bodies on CT sections, and (iv) the
localization of individual vertebral bodies by using a template matching technique. The proposed scheme was applied to
104 CT cases, and its performance was assessed using the Hausdorff distance. The average Hausdorff distance of T2-L5
was 4.3 mm when learning models with 100 samples were used. On the other hand, the average Hausdorff distance with
10 samples was 5.1 mm. The results of our assessments confirmed that the proposed scheme could provide the location
of individual vertebral bodies. Therefore, the proposed scheme may be useful in designing a computer-based application
that analyzes vertebral geometry on body CT images.
Inflammation in the paranasal sinus is often observed in seasonal allergic rhinitis or with colds, but is also an
indication for odontogenic tumors, carcinoma of the maxillary sinus or a maxillary cyst. The detection of those findings
in dental panoramic radiographs is not difficult for radiologists, but general dentists may miss the findings since they
focus on treatments of teeth. The purpose of this work is to develop a contralateral subtraction method for detecting the
odontogenic sinusitis region on dental panoramic radiographs. We developed a contralateral subtraction technique in
paranasal sinus region, consisting of 1) image filtering of the smoothing and sobel operation for noise reduction and edge
extraction, 2) image registration of mirrored image by using mutual information, and 3) image display method of
subtracted pixel data. We employed 56 cases (24 normal and 32 abnormal). The abnormal regions and the normal cases
were verified by a board-certified radiologist using CT scans. Observer studies with and without subtraction images were
performed for 9 readers. The true-positive rate at a 50% confidence level in 7 out of 9 readers was improved, but there
was no statistical significance in the difference of area-under-curve (AUC) in each radiologist. In conclusion, the
contralateral subtraction images of dental panoramic radiographs may improve the detection rate of abnormal regions in
paranasal sinus.
X-ray CT images have been widely used in clinical routine in recent years. CT images scanned by a modern CT
scanner can show the details of various organs and tissues. This means various organs and tissues can be simultaneously
interpreted on CT images. However, CT image interpretation requires a lot of time and energy. Therefore, support for
interpreting CT images based on image-processing techniques is expected. The interpretation of the spinal curvature is
important for clinicians because spinal curvature is associated with various spinal disorders. We propose a quantification
scheme of the spinal curvature based on the center line of spinal canal on CT images. The proposed scheme consists of
four steps: (1) Automated extraction of the skeletal region based on CT number thresholding. (2) Automated extraction
of the center line of spinal canal. (3) Generation of the median plane image of spine, which is reformatted based on the
spinal canal. (4) Quantification of the spinal curvature. The proposed scheme was applied to 10 cases, and compared
with the Cobb angle that is commonly used by clinicians. We found that a high-correlation (for the 95% confidence
interval, lumbar lordosis: 0.81-0.99) between values obtained by the proposed (vector) method and Cobb angle. Also, the
proposed method can provide the reproducible result (inter- and intra-observer variability: within 2°). These
experimental results suggested a possibility that the proposed method was efficient for quantifying the spinal curvature
on CT images.
X-ray CT images have been widely used in clinical diagnosis in recent years. A modern CT scanner can generate
about 1000 CT slices to show the details of all the human organs within 30 seconds. However, CT image interpretations
(viewing 500-1000 slices of CT images manually in front of a screen or films for each patient) require a lot of time and
energy. Therefore, computer-aided diagnosis (CAD) systems that can support CT image interpretations are strongly
anticipated. Automated recognition of the anatomical structures in CT images is a basic pre-processing of the CAD
system. The bone structure is a part of anatomical structures and very useful to act as the landmarks for predictions of the
other different organ positions. However, the automated recognition of the bone structure is still a challenging issue. This
research proposes an automated scheme for segmenting the bone regions and recognizing the bone structure in noncontrast
torso CT images. The proposed scheme was applied to 48 torso CT cases and a subjective evaluation for the
experimental results was carried out by an anatomical expert following the anatomical definition. The experimental
results showed that the bone structure in 90% CT cases have been recognized correctly. For quantitative evaluation,
automated recognition results were compared to manual inputs of bones of lower limb created by an anatomical expert
on 10 randomly selected CT cases. The error (maximum distance in 3D) between the recognition results and manual
inputs distributed from 3-8 mm in different parts of the bone regions.
Computer-aided diagnosis (CAD) has been expected to help radiologists to improve the accuracy of abnormality detection and reduce the burden during CT image interpretations. In order to realize such functions, automated segmentations of the target organ regions are always required by CAD systems. This paper describes a fully automatic processing procedure, which is designed to identify inter-lobe fissures and divide lung into five lobe regions. The lung fissures are disappeared very fuzzy and indefinite in CT images, so that it is very difficult to extract fissures directly based on its CT values. We propose a method to solve this problem using the anatomy knowledge of human lung. We extract lung region firstly and then recognize the structures of lung vessels and bronchus. Based on anatomy knowledge, we classify the vessels and bronchus on a lobe-by-lobe basis and estimate the boundary of each lobe region as the initial fissure locations. Within those locations, we extract lung fissures precisely based on an edge detection method and divide lung regions into five lung lobes lastly. The performance of the proposed method was evaluated using 9 patient cases of high-resolution multi-slice chest CT images; the improvement has been confirmed with the reliable recognition results.
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