Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

A new method for improved standardisation in three-dimensional computed tomography cephalometry

Interest for three-dimensional computed tomography cephalometry has risen over the last two decades. Current methods commonly rely on the examiner to manually point-pick the landmarks and/or orientate the skull. In this study, a new approach is presented, in which landmarks are calculated after selection of the landmark region on a triangular model and in which the skull is automatically orientated in a standardised way. Two examiners each performed five analyses on three skull models. Landmark reproducibility was tested by calculating the standard deviation for each observer and the difference between the mean values of both observers. The variation can be limited to 0.1 mm for most landmarks. However, some landmarks perform less well and require further investigation. With the proposed reference system, a symmetrical orientation of the skulls is obtained. The presented methods contribute to standardisation in cephalometry and could therefore allow improved comparison of patient data

The reliability of cephalometric tracing using AI

Introduction : L'objectif de cette étude est de comparer la différence entre l'analyse céphalométrique manuelle et l'analyse automatisée par l’intelligence artificielle afin de confirmer la fiabilité de cette dernière. Notre hypothèse de recherche est que la technique manuelle est la plus fiable des deux méthodes. Méthode : Un total de 99 radiographies céphalométriques latérales sont recueillies. Des tracés par technique manuelle (MT) et par localisation automatisée par intelligence artificielle (AI) sont réalisés pour toutes les radiographies. La localisation de 29 points céphalométriques couramment utilisés est comparée entre les deux groupes. L'erreur radiale moyenne (MRE) et un taux de détection réussie (SDR) de 2 mm sont utilisés pour comparer les deux groupes. Le logiciel AudaxCeph version 6.2.57.4225 est utilisé pour l'analyse manuelle et l'analyse AI. Résultats : Le MRE et SDR pour le test de fiabilité inter-examinateur sont respectivement de 0,87 ± 0,61mm et 95%. Pour la comparaison entre la technique manuelle MT et le repérage par intelligence artificielle AI, le MRE et SDR pour tous les repères sont respectivement de 1,48 ± 1,42 mm et 78 %. Lorsque les repères dentaires sont exclus, le MRE diminue à 1,33 ± 1,39 mm et le SDR augmente à 84 %. Lorsque seuls les repères des tissus durs sont inclus (excluant les points des tissus mous et dentaires), le MRE diminue encore à 1,25 ± 1,09 mm et le SDR augmente à 85 %. Lorsque seuls les points de repère des tissus mous sont inclus, le MRE augmente à 1,68 ± 1,89 mm et le SDR diminue à 78 %. Conclusion: La performance du logiciel est similaire à celles précédemment rapportée dans la littérature pour des logiciels utilisant un cadre de modélisation similaire. Nos résultats révèlent que le repérage manuel a donné lieu à une plus grande précision. Le logiciel a obtenu de très bons résultats pour les points de tissus durs, mais sa précision a diminué pour les tissus mous et dentaires. Nous concluons que cette technologie est très prometteuse pour une application en milieu clinique sous la supervision du docteur.Introduction: The objective of this study is to compare the difference between manual cephalometric analysis and automatic analysis by artificial intelligence to confirm the reliability of the latter. Our research hypothesis is that the manual technique is the most reliable of the methods and is still considered the gold standard. Method: A total of 99 lateral cephalometric radiographs were collected in this study. Manual technique (MT) and automatic localization by artificial intelligence (AI) tracings were performed for all radiographs. The localization of 29 commonly used landmarks were compared between both groups. Mean radial error (MRE) and a successful detection rate (SDR) of 2mm were used to compare both groups. AudaxCeph software version 6.2.57.4225 (Audax d.o.o., Ljubljana, Slovenia) was used for both manual and AI analysis. Results: The MRE and SDR for the inter-examinator reliability test were 0.87 ± 0.61mm and 95% respectively. For the comparison between the manual technique MT and landmarking with artificial intelligence AI, the MRE and SDR for all landmarks were 1.48 ± 1.42mm and 78% respectively. When dental landmarks are excluded, the MRE decreases to 1.33 ± 1.39mm and the SDR increases to 84%. When only hard tissue landmarks are included (excluding soft tissue and dental points) the MRE decreases further to 1.25 ± 1.09mm and the SDR increases to 85%. When only soft tissue landmarks are included the MRE increases to 1.68 ± 1.89mm and the SDR decreases to 78%. Conclusion: The software performed similarly to what was previously reported in literature for software that use analogous modeling framework. Comparing the software’s landmarking to manual landmarking our results reveal that the manual landmarking resulted in higher accuracy. The software operated very well for hard tissue points, but its accuracy went down for soft and dental tissue. Our conclusion is this technology shows great promise for application in clinical settings under the doctor’s supervision

'Aariz: A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification

The accurate identification and precise localization of cephalometric landmarks enable the classification and quantification of anatomical abnormalities. The traditional way of marking cephalometric landmarks on lateral cephalograms is a monotonous and time-consuming job. Endeavours to develop automated landmark detection systems have persistently been made, however, they are inadequate for orthodontic applications due to unavailability of a reliable dataset. We proposed a new state-of-the-art dataset to facilitate the development of robust AI solutions for quantitative morphometric analysis. The dataset includes 1000 lateral cephalometric radiographs (LCRs) obtained from 7 different radiographic imaging devices with varying resolutions, making it the most diverse and comprehensive cephalometric dataset to date. The clinical experts of our team meticulously annotated each radiograph with 29 cephalometric landmarks, including the most significant soft tissue landmarks ever marked in any publicly available dataset. Additionally, our experts also labelled the cervical vertebral maturation (CVM) stage of the patient in a radiograph, making this dataset the first standard resource for CVM classification. We believe that this dataset will be instrumental in the development of reliable automated landmark detection frameworks for use in orthodontics and beyond

An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images

Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE) were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged

CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023

Quantitative cephalometric analysis is the most widely used clinical and research tool in modern orthodontics. Accurate localization of cephalometric landmarks enables the quantification and classification of anatomical abnormalities, however, the traditional manual way of marking these landmarks is a very tedious job. Endeavours have constantly been made to develop automated cephalometric landmark detection systems but they are inadequate for orthodontic applications. The fundamental reason for this is that the amount of publicly available datasets as well as the images provided for training in these datasets are insufficient for an AI model to perform well. To facilitate the development of robust AI solutions for morphometric analysis, we organise the CEPHA29 Automatic Cephalometric Landmark Detection Challenge in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI 2023). In this context, we provide the largest known publicly available dataset, consisting of 1000 cephalometric X-ray images. We hope that our challenge will not only derive forward research and innovation in automatic cephalometric landmark identification but will also signal the beginning of a new era in the discipline

Automatic Cephalometric Analysis

Abstract Objective: To describe the techniques used for automatic landmarking of cephalograms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in locating each cephalometric point. Materials and Methods: The literature survey was performed by searching the Medline, the Institute of Electrical and Electronics Engineers, and the ISI Web of Science Citation Index databases. The survey covered the period from January 1966 to August 2006. Abstracts that appeared to fulfill the initial selection criteria were selected by consensus. The original articles were then retrieved. Their references were also hand-searched for possible missing articles. The search strategy resulted in 118 articles of which eight met the inclusion criteria. Many articles were rejected for different reasons; among these, the most frequent was that results of accuracy for automatic landmark recognition were presented as a percentage of success. Results: A marked difference in results was found between the included studies consisting of heterogeneity in the performance of techniques to detect the same landmark. All in all, hybrid approaches detected cephalometric points with a higher accuracy in contrast to the results for the same points obtained by the model-based, image filtering plus knowledge-based landmark search and "soft-computing" approaches. Conclusions: The systems described in the literature are not accurate enough to allow their use for clinical purposes. Errors in landmark detection were greater than those expected with manual tracing and, therefore, the scientific evidence supporting the use of automatic landmarking is low