Semi-automatic registration of 3D orthodontics models from photographs

International audienceIn orthodontics, a common practice used to diagnose and plan the treatment is the dental cast. After digitization by a CT-scan or a laser scanner, the obtained 3D surface models can feed orthodontics numerical tools for computer-aided diagnosis and treatment planning. One of the pre-processing critical steps is the 3D registration of dental arches to obtain the occlusion of these numerical models. For this task, we propose a vision based method to automatically compute the registration based on photos of patient mouth. From a set of matched singular points between two photos and the dental 3D models, the rigid transformation to apply to the mandible to be in contact with the maxillary may be computed by minimizing the reprojection errors. During a precedent study, we established the feasibility of this visual registration approach with a manual selection of singular points. This paper addresses the issue of automatic point detection. Based on a priori knowledge, histogram thresholding and edge detection are used to extract specific points in 2D images. Concurrently, curvatures information detects 3D corresponding points. To improve the quality of the final registration, we also introduce a combined optimization of the projection matrix with the 2D/3D point positions. These new developments are evaluated on real data by considering the reprojection errors and the deviation angles after registration in respect to the manual reference occlusion realized by a specialist. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

How to Obtain an Orthodontic Virtual Patient through Superimposition of Three-Dimensional Data: A Systematic Review

Background: This systematic review summarizes the current knowledge on the superimposition of three-dimensional (3D) diagnostic records to realize an orthodontic virtual patient. The aim of this study is to analyze the accuracy of the state-of-the-art digital workflow. Methods: The research was carried out by an electronic and manual query eectuated from ISS (Istituto Superiore di Sanit\ue0 in Rome) on three dierent databases (MEDLINE, Cochrane Library and ISI WEB OF SCIENCE) up to 31st January 2020. The search focused on studies that superimposed at least two dierent 3D records to build up a 3D virtual patient\u2014information about the devices used to acquire 3D data, the software used to match data and the superimposition method applied have been summarized. Results: 1374 titles were retrieved from the electronic search. After title-abstract screening, 65 studies were selected. After full-text analysis, 21 studies were included in the review. Dierent 3D datasets were used: facial skeleton (FS), extraoral soft tissues (ST) and dentition (DENT). The information provided by the 3D data was superimposed in four dierent combinations: FS + DENT (13 papers), FS + ST (5 papers), ST + DENT (2 papers) and all the types (FS + ST + DENT) (1 paper). Conclusions: The surface-based method was most frequently used for 3D objects superimposition (11 papers), followed by the point-based method (6 papers), with or without fiducial markers, and the voxel-based method (1 paper). Most of the papers analyzed the accuracy of the superimposition procedure (15 papers), while the remaining were proof-of-principles (10 papers) or compared dierent methods (3 papers). Further studies should focus on the definition of a gold standard. The patient is going to have a huge advantage from complete digital planning when more information about the spatial relationship of anatomical structures are needed: ectopic, impacted and supernumerary teeth, root resorption and angulations, cleft lip and palate (CL/P), alveolar boundary conditions, periodontally compromised patients, temporary anchorage devices (TADs), maxillary transverse deficiency, airway analyses, obstructive sleep apnea (OSAS), TMJ disorders and orthognathic and cranio-facial surgery

Fully automated landmarking and facial segmentation on 3D photographs

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs by a single observer. The automated landmarking workflow involved two successive DiffusionNet models and additional algorithms for facial segmentation. The dataset was randomly divided into a training and test dataset. The training dataset was used to train the deep learning networks, whereas the test dataset was used to evaluate the performance of the automated workflow. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and the semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 (+/-1.15) mm was comparable to the inter-observer variability (1.31 +/-0.91 mm) of manual annotation. The Euclidean distance between the automated and manual landmarks was within 2 mm in 69%. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.Comment: 13 pages, 4 figures, 7 tables, repository https://github.com/rumc3dlab/3dlandmarkdetection

3D superimposition of craniofacial imaging—The utility of multicentre collaborations

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149360/1/ocr12281.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149360/2/ocr12281_am.pd

Clinical Computing in Dentistry

Machines can seldom replace dentists in rightly handling the patients with optimistic human insight, considerations, creative planning and the monitoring of psychological acceptance and comfort experienced by any patient with the rehabilitation done. Intelligent computer related armamentarium with software can still help dental practitioners detect typical medical and dental signs and classify them according to certain rules more effectively. Based on image analysis algorithms, CAD systems can be used to look for signs of any tooth pathology that can be spotted in dental X-ray or cone beam computed tomography (CBCT) images. Applying computer vision algorithms to high-resolution CBCT slices helps to a great extent in diagnosing periapical lesions like granulomas, cysts, etc., and can help creating 3-D model of a root canal that reflects its shape with sufficient precision facilitating an optimum endodontic treatment planning. Hence, computer vision systems are already able to speed up the diagnostic process and provide a valuable second opinion in doubtful cases. This can lead a dentist and the patient thoroughly experience an optimistic acceptance and satisfaction of the treatment done

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Objectives The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. Methods PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication. Results The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I-2 = 98.13%, tau(2) = 1.018, p-value < 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012). Conclusion Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done

A pilot study for the digital replacement of a distorted dentition acquired by Cone Beam Computed Tomography (CBCT)

Abstract Introduction: Cone beam CT (CBCT) is becoming a routine imaging modality designed for the maxillofacial region. Imaging patients with intra-oral metallic objects cause streak artefacts. Artefacts impair any virtual model by obliterating the teeth. This is a major obstacle for occlusal registration and the fabrication of orthognathic wafers to guide the surgical correction of dentofacial deformities. Aims and Objectives: To develop a method of replacing the inaccurate CBCT images of the dentition with an accurate representation and test the feasibility of the technique in the clinical environment. Materials and Method: Impressions of the teeth are acquired and acrylic baseplates constructed on dental casts incorporating radiopaque registration markers. The appliances are fitted and a preoperative CBCT is performed. Impressions are taken of the dentition with the devices in situ and subsequent dental models produced. The models are scanned to produce a virtual model. Both images of the patient and the model are imported into a virtual reality software program and aligned on the virtual markers. This allows the alignment of the dentition without relying on the teeth for superimposition. The occlusal surfaces of the dentition can be replaced with the occlusal image of the model. Results: The absolute mean distance of the mesh between the markers in the skulls was in the region of 0.09mm ± 0.03mm; the replacement dentition had an absolute mean distance of about 0.24mm ± 0.09mm. In patients the absolute mean distance between markers increased to 0.14mm ± 0.03mm. It was not possible to establish the discrepancies in the patient’s dentition, since the original image of the dentition is inherently inaccurate. Conclusion: It is possible to replace the CBCT virtual dentition of cadaveric skulls with an accurate representation to create a composite skull. The feasibility study was successful in the clinical arena. This could be a significant advancement in the accuracy of surgical prediction planning, with the ultimate goal of fabrication of a physical orthognathic wafer using reverse engineering