Automatic Landmark Detection Research Articles

An automated framework for pediatric hip surveillance and severity assessment using radiographs.

Hip dysplasia is the second most common orthopedic condition in children with cerebral palsy (CP) and may result in disability and pain. The migration percentage (MP) is a widely used metric in hip surveillance, calculated based on an anterior-posterior pelvis radiograph. However, manual quantification of MP values using hip X-ray scans in current standard practice has challenges including being time-intensive, requiring expert knowledge, and not considering human bias. The purpose of this study is to develop a machine learning algorithm to automatically quantify MP values using a hip X-ray scan, and hence provide an assessment for severity, which then can be used for surveillance, treatment planning, and management. X-ray scans from 210 patients were curated, pre-processed, and manually annotated at our clinical center. Several machine learning models were trained using pre-trained weights from Inception ResNet-V2, VGG-16, and VGG-19, with different strategies (pre-processing, with and without region of interest (ROI) detection, with and without data augmentation) to find an optimal model for automatic hip landmarking. The predicted landmarks were then used by our geometric algorithm to quantify the MP value for the input hip X-ray scan. The pre-trained VGG-19 model, fine-tuned with additional custom layers, outputted the lowest mean squared error values for both train and test data, when ROI cropped images were used along with data augmentation for model training. The MP value calculated by the algorithm was compared to manual ground truth labels from our orthopedic fellows using the hip screen application for benchmarking. The results showed the feasibility of the machine learning model in automatic hip landmark detection for reliably quantifying MP value from hip X-ray scans. The algorithm could be used as an accurate and reliable tool in orthopedic care for diagnosing, severity assessment, and hence treatment and surgical planning for hip displacement.

Automatic soft-tissue analysis on orthodontic frontal and lateral facial photographs based on deep learning.

To establish the automatic soft-tissue analysis model based on deep learning that performs landmark detection and measurement calculations on orthodontic facial photographs to achieve a more comprehensive quantitative evaluation of soft tissues. A total of 578 frontal photographs and 450 lateral photographs of orthodontic patients were collected to construct datasets. All images were manually annotated by two orthodontists with 43 frontal-image landmarks and 17 lateral-image landmarks. Automatic landmark detection models were established, which consisted of a high-resolution network, a feature fusion module based on depthwise separable convolution, and a prediction model based on pixel shuffle. Ten measurements for frontal images and eight measurements for lateral images were defined. Test sets were used to evaluate the model performance, respectively. The mean radial error of landmarks and measurement error were calculated and statistically analysed to evaluate their reliability. The mean radial error was 14.44 ± 17.20 pixels for the landmarks in the frontal images and 13.48 ± 17.12 pixels for the landmarks in the lateral images. There was no statistically significant difference between the model prediction and manual annotation measurements except for the mid facial-lower facial height index. A total of 14 measurements had a high consistency. Based on deep learning, we established automatic soft-tissue analysis models for orthodontic facial photographs that can automatically detect 43 frontal-image landmarks and 17 lateral-image landmarks while performing comprehensive soft-tissue measurements. The models can assist orthodontists in efficient and accurate quantitative soft-tissue evaluation for clinical application.

ChatGPT in orthodontics: limitations and possibilities

Abstract Artificial Intelligence (AI) simulates human intelligence in machines capable of rational response and actions. AI’s growth in healthcare, particularly in dentistry, has been remarkable in aiding clinical practices from appointment scheduling to treatment outcome predictions. In orthodontics, AI effectively handles tasks such as automated landmark detection, diagnosing orthodontic needs, assessing maxillary constriction, and selecting appliances. ChatGPT, an AI-based program by OpenAI, excels in generating human-like responses, making it useful across many applications, including those in dentistry. AI enhances personalised patient communication, supports diagnosis and decision-making, digitises patient data, and aids in disease prevention and treatment. In addition, ChatGPT offers support in personalised care, billing, and patient health monitoring. Patients benefit from its help in finding providers, scheduling initial appointments, accessing health information, improving healthcare access and service effectiveness. However, there are limitations. ChatGPT sometimes produces nonsensical responses and poses privacy risks associated with patient data. Generated medical advice might not therefore match professional expertise. Despite these issues, if trained properly, ChatGPT could significantly enhance medical knowledge dissemination. Future studies should weigh the benefits of AI against its risks, and users must verify AI-generated information with reliable sources to ensure effective patient care.

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. Studies that employed AI for 2D or 3D cephalometric landmark detection were included. The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2mm (1.39mm; 95% confidence interval: 0.85-1.92mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. PROSPERO: CRD42022328800.

Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data, and Symptomatic Assessments.

In this study, we propose a novel framework that utilizes deep learning and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of 7 years. This study included subjects (1,832 subjects, 3,276 knees) from the baseline of the Multicenter Osteoarthritis Study (MOST). Patellofemoral joint regions of interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. An end-to-end deep learning method was developed for predicting PFOA progression based on imaging data in a five-fold cross-validation setting. To evaluate the performance of the models, a set of baselines based on known risk factors were developed and analyzed using gradient boosting machine (GBM). Risk factors included age, sex, body mass index, and Western Ontario and McMaster Universities Arthritis Index score, and the radiographic osteoarthritis stage of the tibiofemoral joint (Kellgren and Lawrence [KL] score). Finally, to increase predictive power, we trained an ensemble model using both imaging and clinical data. Among the individual models, the performance of our deep convolutional neural network attention model achieved the best performance with an area under the receiver operating characteristic curve (AUC) of 0.856 and average precision (AP) of 0.431, slightly outperforming the deep learning approach without attention (AUC = 0.832, AP = 0.4) and the best performing reference GBM model (AUC = 0.767, AP = 0.334). The inclusion of imaging data and clinical variables in an ensemble model allowed statistically more powerful prediction of PFOA progression (AUC = 0.865, AP = 0.447), although the clinical significance of this minor performance gain remains unknown. The spatial attention module improved the predictive performance of the backbone model, and the visual interpretation of attention maps focused on the joint space and the regions where osteophytes typically occur. This study demonstrated the potential of machine learning models to predict the progression of PFOA using imaging and clinical variables. These models could be used to identify patients who are at high risk of progression and prioritize them for new treatments. However, even though the accuracy of the models were excellent in this study using the MOST dataset, they should be still validated using external patient cohorts in the future.

A deep learning-based system for accurate detection of anatomical landmarks in colon environment

Colonoscopy is a standard imaging tool for examining the lower gastrointestinal tract of patients to capture lesion areas. However, if a lesion area is found during the colonoscopy process, it is difficult to record its location relative to the colon for subsequent therapy or recheck without any reference landmark. Thus, automatic detection of biological anatomical landmarks is highly demanded to improve clinical efficiency. In this article, we propose a novel deep learning-based approach to detect biological anatomical landmarks in colonoscopy videos. First, raw colonoscopy video sequences are pre-processed to reject interference frames. Second, a ResNet-101-based network is used to detect three biological anatomical landmarks separately to obtain the intermediate detection results. Third, to achieve more reliable localization, we propose to post-process the intermediate detection results by identifying the incorrectly predicted frames based on their temporal distribution and reassigning them back to the correct class. Finally, the average detection accuracy reaches 99.75%. Meanwhile, the average intersection over union of 0.91 shows a high degree of similarity between our predicted landmark periods and ground truth. The experimental results demonstrate that our proposed model can accurately detect and localize biological anatomical landmarks from colonoscopy videos.

Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment

Purpose:An efficient physics-informed deep learning approach for extracting spinopelvic measures from X-ray images is introduced and its performance is evaluated against manual annotations.Methods:Two datasets, comprising a total of 1470 images, were collected to evaluate the model’s performance. We propose a novel method of detecting landmarks as objects, incorporating their relationships as constraints (LanDet). Using this approach, we trained our deep learning model to extract five spine and pelvis measures: Sacrum Slope (SS), Pelvic Tilt (PT), Pelvic Incidence (PI), Lumbar Lordosis (LL), and Sagittal Vertical Axis (SVA). The results were compared to manually labelled test dataset (GT) as well as measures annotated separately by three surgeons.Results:The LanDet model was evaluated on the two datasets separately and on an extended dataset combining both. The final accuracy for each measure is reported in terms of Mean Absolute Error (MAE), Standard Deviation (SD), and R Pearson correlation coefficient as follows: [SS∘:3.7(2.7),R=0.89]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[SS^\\circ : 3.7 (2.7), R = 0.89]$$\\end{document}, [PT∘:1.3(1.1),R=0.98],[PI∘:4.2(3.1),R=0.93],[LL∘:5.1(6.4),R=0.83],[SVA(mm):2.1(1.9),R=0.96]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[PT^\\circ : 1.3 (1.1), R = 0.98], [PI^\\circ : 4.2 (3.1), R = 0.93], [LL^\\circ : 5.1 (6.4), R=0.83], [SVA(mm): 2.1 (1.9), R = 0.96]$$\\end{document}. To assess model reliability and compare it against surgeons, the intraclass correlation coefficient (ICC) metric is used. The model demonstrated better consistency with surgeons with all values over 0.88 compared to what was previously reported in the literature.Conclusion:The LanDet model exhibits competitive performance compared to existing literature. The effectiveness of the physics-informed constraint method, utilized in our landmark detection as object algorithm, is highlighted. Furthermore, we addressed the limitations of heatmap-based methods for anatomical landmark detection and tackled issues related to mis-identifying of similar or adjacent landmarks instead of intended landmark using this novel approach.

Automatic detection and labeling of glides for the English and Spanish language

This study outlines the development of a module for automatic detection of glide landmarks. We define glide landmarks as acoustic events that are observed during a narrowing in the oral cavity that does not result in cessation of airflow or in the conditions for turbulence noise. Glide landmarks are commonly associated with standard productions of underlying glides, a set that includes semivowels, liquids, and sounds produced by narrowing at the glottis, such as glottal stops, or the aspirant /h/. We lay out a framework that can be used to determine the acoustic measurements that are useful for detecting glide landmarks, and a Gausian Mixture Model is trained and tested, for automatic detection. A first investigation is carried out for /r/ production in English and Spanish. A closer look reveals that acoustic cues that describe abrupt onsets and offsets is useful for describing the trilled /r/ in Spanish, resulting in the need for an additional set of glide landmark cues for glide closure and glide release.

Multi-task global optimization-based method for vascular landmark detection

Vascular landmark detection plays an important role in medical analysis and clinical treatment. However, due to the complex topology and similar local appearance around landmarks, the popular heatmap regression based methods always suffer from the landmark confusion problem. Vascular landmarks are connected by vascular segments and have special spatial correlations, which can be utilized for performance improvement. In this paper, we propose a multi-task global optimization-based framework for accurate and automatic vascular landmark detection. A multi-task deep learning network is exploited to accomplish landmark heatmap regression, vascular semantic segmentation, and orientation field regression simultaneously. The two auxiliary objectives are highly correlated with the heatmap regression task and help the network incorporate the structural prior knowledge. During inference, instead of performing a max-voting strategy, we propose a global optimization-based post-processing method for final landmark decision. The spatial relationships between neighboring landmarks are utilized explicitly to tackle the landmark confusion problem. We evaluated our method on a cerebral MRA dataset with 564 volumes, a cerebral CTA dataset with 510 volumes, and an aorta CTA dataset with 50 volumes. The experiments demonstrate that the proposed method is effective for vascular landmark localization and achieves state-of-the-art performance.

Deep learning for automatic detection of cephalometric landmarks on lateral cephalometric radiographs using the Mask Region-based Convolutional Neural Network: a pilot study

ObjectiveWe examined the effectiveness and feasibility of the Mask Region-based Convolutional Neural Network (Mask R-CNN) for automatic detection of cephalometric landmarks on lateral cephalometric radiographs (LCRs). Study DesignIn total, 400 LCRs, each with 19 manually identified landmarks, were collected. Of this total, 320 images were randomly selected as the training dataset for Mask R-CNN, and the remaining 80 images were used for testing the automatic detection of the 19 cephalometric landmarks, for a total of 1520 landmarks. Detection rate, average error, and detection accuracy rate were calculated to assess Mask R-CNN performance. ResultsOf the 1520 landmarks, 1494 were detected, for a detection rate of 98.29%. The average error, or linear deviation distance between the detected points and the originally marked points of each detected landmark, ranged from 0.56 to 9.51 mm, with an average of 2.19 mm. For detection accuracy rate, 649 landmarks (43.44%) had a linear deviation distance less than 1mm, 1020 (68.27%) less than 2mm, and 1281 (85.74%) less than 4mm in deviation from the manually marked point. The average detection time was 1.48 seconds per image. ConclusionsDeep learning Mask R-CNN shows promise in enhancing cephalometric analysis by automating landmark detection on LCRs, addressing the limitations of manual analysis and demonstrating effectiveness and feasibility.

Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm.

This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which were determined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The time required to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually, compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculate these measurements, the efficiency of diagnosis and treatment may be improved.

Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model

BackgroundAccurate cephalometric analysis plays a vital role in the diagnosis and subsequent surgical planning in orthognathic and orthodontics treatment. However, manual digitization of anatomical landmarks in computed tomography (CT) is subject to limitations such as low accuracy, poor repeatability and excessive time consumption. Furthermore, the detection of landmarks has more difficulties on individuals with dentomaxillofacial deformities than normal individuals. Therefore, this study aims to develop a deep learning model to automatically detect landmarks in CT images of patients with dentomaxillofacial deformities.MethodsCraniomaxillofacial (CMF) CT data of 80 patients with dentomaxillofacial deformities were collected for model development. 77 anatomical landmarks digitized by experienced CMF surgeons in each CT image were set as the ground truth. 3D UX-Net, the cutting-edge medical image segmentation network, was adopted as the backbone of model architecture. Moreover, a new region division pattern for CMF structures was designed as a training strategy to optimize the utilization of computational resources and image resolution. To evaluate the performance of this model, several experiments were conducted to make comparison between the model and manual digitization approach.ResultsThe training set and the validation set included 58 and 22 samples respectively. The developed model can accurately detect 77 landmarks on bone, soft tissue and teeth with a mean error of 1.81 ± 0.89 mm. Removal of region division before training significantly increased the error of prediction (2.34 ± 1.01 mm). In terms of manual digitization, the inter-observer and intra-observer variations were 1.27 ± 0.70 mm and 1.01 ± 0.74 mm respectively. In all divided regions except Teeth Region (TR), our model demonstrated equivalent performance to experienced CMF surgeons in landmarks detection (p > 0.05).ConclusionsThe developed model demonstrated excellent performance in detecting craniomaxillofacial landmarks when considering manual digitization work of expertise as benchmark. It is also verified that the region division pattern designed in this study remarkably improved the detection accuracy.

Ceph-Net: automatic detection of cephalometric landmarks on scanned lateral cephalograms from children and adolescents using an attention-based stacked regression network

BackgroundThe success of cephalometric analysis depends on the accurate detection of cephalometric landmarks on scanned lateral cephalograms. However, manual cephalometric analysis is time-consuming and can cause inter- and intra-observer variability. The purpose of this study was to automatically detect cephalometric landmarks on scanned lateral cephalograms with low contrast and resolution using an attention-based stacked regression network (Ceph-Net).MethodsThe main body of Ceph-Net compromised stacked fully convolutional networks (FCN) which progressively refined the detection of cephalometric landmarks on each FCN. By embedding dual attention and multi-path convolution modules in Ceph-Net, the network learned local and global context and semantic relationships between cephalometric landmarks. Additionally, the intermediate deep supervision in each FCN further boosted the training stability and the detection performance of cephalometric landmarks.ResultsCeph-Net showed a superior detection performance in mean radial error and successful detection rate, including accuracy improvements in cephalometric landmark detection located in low-contrast soft tissues compared with other detection networks. Moreover, Ceph-Net presented superior detection performance on the test dataset split by age from 8 to 16 years old.ConclusionsCeph-Net demonstrated an automatic and superior detection of cephalometric landmarks by successfully learning local and global context and semantic relationships between cephalometric landmarks in scanned lateral cephalograms with low contrast and resolutions.

AUTOMATIC DETECTION OF FACIAL LOCATIONS TO MEASURE FACIAL ASYMMETRY AFTER PAEDIATRIC RADIOTHERAPY

Abstract AIMS Children receiving radiotherapy for head and neck tumours, such as Head and Neck Rhabdomyosarcoma (HN- RMS), often experience facial asymmetry later in life. Here, we present a convolutional neural network (CNN) that will enable the automatic detection of facial anatomical landmarks on MR images taken during follow-up examinations. This model will facilitate quantitative tracking of facial asymmetry, thus revealing the centres of growth that are most affected by radiation, with the ultimate goal of defining precise dose tolerance levels. METHOD A dataset of 61 paediatric MRI images was used to train separate models that infer the locations of four facial landmarks. These locations had previously been manually labelled by two independent observers. Before training, the images were normalised and registered to optimise model performance. The model was judged to be of acceptable accuracy if the distances between the observed landmark (gold-standard location) and the location predicted by the trained models were smaller than the inter-observer distances of each facial landmark. RESULTS The model predicted landmarks which were closer to the gold-standard location than the inter-observer locations. In detail: • Optic chiasm: 0.81 ± 0.45 mm (inter-observer 1.49 mm). • Nasion: 0.70 ± 0.62 mm (inter-observer 3.24 mm). • Left Ectoconchion: 1.22 ± 0.44 mm (inter-observer 3.58 mm). • Right Ectoconchion: 0.80 ± 0.82 mm (inter-observer 4.04 mm). CONCLUSIONS This research shows promising potential for a CNN to accurately locate head and neck landmarks in paediatric MRI images. This tool can label craniofacial landmarks in large clinical datasets quicker and more accurately than can be achieved through manual techniques.

Automated Cephalometric Landmark Detection Using Deep Reinforcement Learning.

Accurate cephalometric landmark detection leads to accurate analysis, diagnosis, and surgical planning. Many studies on automated landmark detection have been conducted, however reinforcement learning-based networks have not yet been applied. This is the first study to apply deep Q-network (DQN) and double deep Q-network (DDQN) to automated cephalometric landmark detection to the best of our knowledge. The performance of the DQN-based network for cephalometric landmark detection was evaluated using the IEEE International Symposium of Biomedical Imaging (ISBI) 2015 Challenge data set and compared with the previously proposed methods. Furthermore, the clinical applicability of DQN-based automated cephalometric landmark detection was confirmed by testing the DQN-based and DDQN-based network using 500-patient data collected in a clinic. The DQN-based network demonstrated that the average mean radius error of 19 landmarks was smaller than 2mm, that is, the clinically accepted level, without data augmentation and additional preprocessing. Our DQN-based and DDQN-based approaches tested with the 500-patient data set showed the average success detection rate of 67.33% and 66.04% accuracy within 2mm, respectively, indicating the feasibility and potential of clinical application.

Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence

ObjectivePatients with rotator cuff tears present often with glenohumeral joint instability. Assessing anatomic angles and shoulder kinematics from fluoroscopy requires labelling of specific landmarks in each image. This study aimed to develop an artificial intelligence model for automatic landmark detection from fluoroscopic images for motion tracking of the scapula and humeral head.Materials and methodsFluoroscopic images were acquired for both shoulders of 25 participants (N = 12 patients with unilateral rotator cuff tear, 6 men, mean (standard deviation) age: 63.7 ± 9.7 years; 13 asymptomatic subjects, 7 men, 58.2 ± 8.9 years) during a 30° arm abduction and adduction movement in the scapular plane with and without handheld weights of 2 and 4 kg. A 3D full-resolution convolutional neural network (nnU-Net) was trained to automatically locate five landmarks (glenohumeral joint centre, humeral shaft, inferior and superior edges of the glenoid and most lateral point of the acromion) and a calibration sphere.ResultsThe nnU-Net was trained with ground-truth data from 6021 fluoroscopic images of 40 shoulders and tested with 1925 fluoroscopic images of 10 shoulders. The automatic landmark detection algorithm achieved an accuracy above inter-rater variability and slightly below intra-rater variability. All landmarks and the calibration sphere were located within 1.5 mm, except the humeral landmark within 9.6 mm, but differences in abduction angles were within 1°.ConclusionThe proposed algorithm detects the desired landmarks on fluoroscopic images with sufficient accuracy and can therefore be applied to automatically assess shoulder motion, scapular rotation or glenohumeral translation in the scapular plane.Clinical relevance statementThis nnU-net algorithm facilitates efficient and objective identification and tracking of anatomical landmarks on fluoroscopic images necessary for measuring clinically relevant anatomical configuration (e.g. critical shoulder angle) and enables investigation of dynamic glenohumeral joint stability in pathological shoulders.Key Points• Anatomical configuration and glenohumeral joint stability are often a concern after rotator cuff tears.• Artificial intelligence applied to fluoroscopic images helps to identify and track anatomical landmarks during dynamic movements.• The developed automatic landmark detection algorithm optimised the labelling procedures and is suitable for clinical application.

Threshold-Based Segmentation for Landmark Detection Using CBCT Images

The aim of this study is to examine the influence of threshold-based segmentation on the mean error of automatic landmark detection in 3D CBCT images. A GUI was developed for radiologists, allowing manual landmark identification and visualization of CBCT images. After a threshold-based segmentation, a semi-automatic algorithm for landmark detection was designed using the anatomic definition of each landmark. A step of 50 Hounsfield units was used for threshold variation to assess the detection error. 5 CBCT images were used to validate the proposed approach. The measurement of error detection for one patient was influenced by the threshold variation. For this patient, the error changed from 1.49 mm to 10.32 mm at a low threshold value, while for another patient, the error changed from 1.96 mm to 12.28 mm at high a threshold value. In a CBCT scanner, the choice of threshold value for segmentation can be an important factor in causing error in measurements.

An automatic cephalometric landmark detection method based on heatmap regression and Monte Carlo dropout.

Cephalometric analysis plays an important role in orthodontic diagnosis and treatment planning. It depends on the detection of multiple landmarks, while the process is time-consuming and tedious. Although some deep learning-based automatic landmark detection algorithms have achieved excellent performance, most of them adopt multi-stage models increasing the complexity and detection time. Meanwhile, few studies focused on the uncertainty of detection results, thereby ignoring its significant clinical value. In this paper, we propose a novel approach based on heatmap regression for landmark detection, which can achieve competitive accuracy and good robustness with only one step. Furthermore, by applying Monte Carlo dropout to a U-shaped convolutional neural network, we can obtain not only the coordinate of each landmark but also the corresponding simple uncertainty, so that doctors can pay more attention to those landmarks with higher uncertainty. The evaluation results showed the mean radial error is 1.39±1.06mm and the average successful detection rate is 79.65%, 97.22% within 2mm, 4mm for the IEEE ISBI2015 Test Dataset 1, the indicators for the IEEE ISBI2015 Test Dataset 2 are 1.33±0.93mm, 80.05% and 97.53%, respectively. Our method has the potential to become an assistant tool in clinical practice. Automatic and accurate detection with uncertainty analysis is expected to help guide the doctor's judgment.

Automatic Labeling of Natural Landmarks for Wheelchair Motion Planning

Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natural landmarks in RGBD images for navigation of mobile platforms in an indoor environment. This method can reduce the time for manually collecting and creating a dataset of landmarks. The wheelchair, equipped with a camera system, is allowed to move along corridors to detect and label natural landmarks automatically. These landmarks contain the camera and wheelchair positions with the 3D coordinates when storing the labeled landmark. The feature density method is comprised of Oriented FAST and Rotated BRIEF (ORB) feature extractors. Moreover, the central coordinates of the marked points in the obtained RGB images will be mapped to the images with the depth axis for determining the position of the RGB-D camera system in the spatial domain. An encoder and kinematics equations are applied to determine the position during movement. As expected, the system shows good results, such as a high IoU value of over 0.8 at a distance of less than 2 m and a fast time of 41.66 ms for object detection. This means that our technique is very effective for the automatic movement of the wheelchair.

Human examination and artificial intelligence in cephalometric landmark detection-is AI ready to take over?

To compare the precision of two cephalometric landmark identification methods, namely a computer-assisted human examination software and an artificial intelligence program, based on South African data. This retrospective quantitative cross-sectional analytical study utilized a data set consisting of 409 cephalograms obtained from a South African population. 19 landmarks were identified in each of the 409 cephalograms by the primary researcher using the two programs [(409 cephalograms x 19 landmarks) x 2 methods = 15,542 landmarks)]. Each landmark generated two coordinate values (x, y), making a total of 31,084 landmarks. Euclidean distances between corresponding pairs of observations was calculated. Precision was determined by using the standard deviation and standard error of the mean. The primary researcher acted as the gold-standard and was calibrated prior to data collection. The inter- and intrareliability tests yielded acceptable results. Variations were present in several landmarks between the two approaches; however, they were statistically insignificant. The computer-assisted examination software was very sensitive to several variables. Several incidental findings were also discovered. Attempts were made to draw valid comparisons and conclusions. There was no significant difference between the two programs regarding the precision of landmark detection. The present study provides a basis to: (1) support the use of automatic landmark detection to be within the range of computer-assisted examination software and (2) determine the learning data required to develop AI systems within an African context.