• The korean journal of orthodontics
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• v.51 no.2
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• pp.77-85
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• 2021

Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.

• The korean journal of orthodontics
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• v.32 no.1 s.90
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• pp.59-69
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• 2002

The purpose of this study was to evaluate the reproducibility of lateral cephalometric landmarks according to radiographic image enhancement, and to contribute to the identification of cephalometric landmarks. Lateral cephalograms of ten individuals were taken and stored into computer. The images were then enhanced up to four grades by Quick Ceph Image Pro$^{TM}$ on condition that the gray-scale equalization number was 50 and the detail enhancement number was 50. After thirty two landmarks were identified on monitor images by five observers, the deviations from the mean, the distances estimated between identified points and the mean point of five identified points, were evaluated for each landmark at each enhancement grade. Through the statistical analysis, following results were obtained. 1. In case of unenhanced radiographic images, the inter-observer reproducibility of the landmarks showed a large variation. 2. The comparison of deviation from the mean according to the degree of radiographic image enhancement for each landmark showed that the inter-observer reproducibility was significantly different at 5 landmarks. 3. The landmark of pterygomaxillary fissure showed higher reproducibility at enhancement grade 1 and 2 images than at unenhanced images. So did the landmark of posterior nasal spine at enhancement grade 1 images, and the landmark of menton at enhancement grade 2, 3 and 4 images respectively. The above results suggest that the reproducibility of some landmarks can be increased by radiographic image enhancement during the identification of the lateral cephalometric landmarks on the monitor.

• Imaging Science in Dentistry
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• v.45 no.4
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• pp.213-220
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• 2015

Purpose: The aim this study was to compare the accuracy of orthodontists and dentomaxillofacial radiologists in identifying 17 commonly used cephalometric landmarks, and to determine the extent of variability associated with each of those landmarks. Materials and Methods: Twenty digital lateral cephalometric radiographs were evaluated by two groups of dental specialists, and 17 cephalometric landmarks were identified. The x and y coordinates of each landmark were recorded. The mean value for each landmark was considered the best estimate and used as the standard. Variation in measurements of the distance between landmarks and measurements of the angles associated with certain landmarks was also assessed by a subset of two observers, and intraobserver and interobserver agreement were evaluated. Results: Intraclass correlation coefficients were excellent for intraobserver agreement, but only good for interobserver agreement. The least reliable landmark for orthodontists was the gnathion (Gn) point (standard deviation [SD], 5.92 mm), while the orbitale (Or) was the least reliable landmark (SD, 4.41 mm) for dentomaxillofacial radiologists. Furthermore, the condylion (Co)-Gn plane was the least consistent (SD, 4.43 mm). Conclusion: We established that some landmarks were not as reproducible as others, both horizontally and vertically. The most consistently identified landmark in both groups was the lower incisor border, while the least reliable points were Co, Gn, Or, and the anterior nasal spine. Overall, a lower level of reproducibility in the identification of cephalometric landmarks was observed among orthodontists.

• The korean journal of orthodontics
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• v.47 no.1
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• pp.50-58
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• 2017

Introduction: Identifying menton (Me) on posteroanterior cephalograms and three-dimensional (3D) cone-beam computed tomography (CBCT) images is difficult, because the midpoint of the symphyseal area is not identifiable after the mandibular symphysis fuses at an early age. The aim of this study was to evaluate the reliability of the identification of the genial tubercle (GT) in patients with mandibular asymmetry and to compare it with that of the traditional landmark, Me. Methods: The samples comprised 20 CBCT images of adults with mandibular asymmetry. Two examiners performed the identifications and measurements. Me and GT were marked, and the anteroposterior, vertical, and transverse distances to the three reference planes were measured on 3D-reconstructed CBCT images. The intra- and inter-examiner reliability of landmark identification of Me and GT were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Results: The Me and GT landmarks showed excellent reliability ($ICC{\geq}0.993$) three-dimensionally. In the transverse evaluation, the ICC values of the GT (range, 0.997-0.999) tended to be slightly higher than those of Me (range, 0.993-0.996). In the Bland-Altman plots for the two separate assessments, Me showed a maximum error of 1.76 mm in the transverse direction, whereas the GT showed a maximum error of 0.96 mm in the 95% limit. Conclusions: Our results suggest that both Me and GT are clinically reliable and equally useful landmarks for the evaluation of mandibular asymmetry on CBCT images.

• Fisheries and Aquatic Sciences
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• v.22 no.6
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• pp.12.1-12.8
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• 2019

Background: Wild fish populations stock is continuously diminishing in the Indo-Ganges river basin, and the population status of most fishes is unidentified. The identification of the population status and the conservation of commercially important and endemic wild fish populations in this region are crucial for the management. The aim of this paper was to identify the population status of Cirrhinus reba, a promising aquaculture but vulnerable species in the Indo-Ganges river basin in Bangladesh. Methods: C. reba samples were collected from four isolated populations of the Brahmaputra (n = 30), the Padma (33), the Karatoya (31), and the Jamuna Rivers (30) in Bangladesh, and the population status was evaluated using morphometric and landmark comparisons. Data were analyzed with the Kruskal-Wallis test, univariate analysis, discriminant function analysis, and the formation of a dendrogram. Results: Three meristic characters (Pectoral fin rays, caudal fin rays, scale in lateral lines), four morphometric characters (head length, pre-orbital length, post-orbital length, maximum body depth), and truss measurement (4-7) were significantly different among the stocks. The step-wise discriminant function analysis retained 15 variables from morphometric and landmark measurements that significantly differentiated the populations based on the constructed DFI and DFII. Discriminate function analysis also showed that 91.2% of the original groups were classified into their correct samples. The cluster analysis of Euclidean distances placed the Jamuna population in one cluster and the Brahmaputra, the Padma, and the Karatoya populations in the second one. Conclusion : Morphological differences among the stock were probably due to different ancestral origin. This is the first report about population status of C. reba in their natural habitat of the Indian subcontinent. Further genetic studies and the evaluation of environmental impact on C. reba populations in Bangladesh are suggested to support our findings.

• Imaging Science in Dentistry
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• v.51 no.3
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• pp.299-306
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• 2021

Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.

• The korean journal of orthodontics
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• v.54 no.1
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• pp.48-58
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• 2024

Objective: To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions: The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.353-364
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• 2009

In this paper, a SLAM (Simultaneous Localization and Mapping) method using a single camera and planar fiducial markers is proposed. Fiducial markers are planar patterns that are mounted on the ceiling or wall. Each fiducial marker has a unique hi-tonal identification pattern with square outlines. It can be printed on paper to reduce cost or it can be painted using retro-reflective paint in order to make invisible and prevent undesirable visual effects. Existing localization methods using artificial landmarks have the disadvantage that landmark locations must be known a priori. In contrast, the proposed method can build a map and estimate robot location even if landmark locations are not known a priori. Hence, it reduces installation time and setup cost. The proposed method works good even when only one fiducial marker is seen at a scene. We perform computer simulation to evaluate proposed method.

• Journal of the Ergonomics Society of Korea
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• v.24 no.2
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• pp.35-43
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• 2005

The purpose of this study is to suggest computer assisted neck-base's landmark identification algorithms and measurement extraction methods from three-dimensional human scan data. So we developed the algorithms for automatic identification of landmarks related to the neck-base types. The subjects were 58 women $18{\sim}24$ years of age. Their body were measured directly and indirectly by using camera and three-dimensional body scanner. They were measured during the months of October in 2001. Based on the characters of classified neck-base types, algorithms for the automatic identification of landmarks and methods of automatic measurement are developed. The three-dimensional automatic measuring program is made by $C^{++}$ language. Using this program, 4 landmarks are identified and 6 items are measured. In the verifying the precision of automatic measurement, the height measurements(cervicale, side neck point, front neck point) were relatively accurate, but neck-base width measurement was measured wide.

• The korean journal of orthodontics
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• v.52 no.4
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• pp.287-297
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• 2022

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.