Automatic image slice marking propagation on segmentation of dental CBCT

Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively

Creation of 3D Multi-Body Orthodontic Models by Using Independent Imaging Sensors

In the field of dental health care, plaster models combined with 2D radiographs are widely used in clinical practice for orthodontic diagnoses. However, complex malocclusions can be better analyzed by exploiting 3D digital dental models, which allow virtual simulations and treatment planning processes. In this paper, dental data captured by independent imaging sensors are fused to create multi-body orthodontic models composed of teeth, oral soft tissues and alveolar bone structures. The methodology is based on integrating Cone-Beam Computed Tomography (CBCT) and surface structured light scanning. The optical scanner is used to reconstruct tooth crowns and soft tissues (visible surfaces) through the digitalization of both patients’ mouth impressions and plaster casts. These data are also used to guide the segmentation of internal dental tissues by processing CBCT data sets. The 3D individual dental tissues obtained by the optical scanner and the CBCT sensor are fused within multi-body orthodontic models without human supervisions to identify target anatomical structures. The final multi-body models represent valuable virtual platforms to clinical diagnostic and treatment planning

Geometrical modeling of complete dental shapes by using panoramic X-ray, digital mouth data and anatomical templates

In the field of orthodontic planning, the creation of a complete digital dental model to simulate and predict treatments is of utmost importance. Nowadays, orthodontists use panoramic radiographs (PAN) and dental crown representations obtained by optical scanning. However, these data do not contain any 3D information regarding tooth root geometries. A reliable orthodontic treatment should instead take into account entire geometrical models of dental shapes in order to better predict tooth movements. This paper presents a methodology to create complete 3D patient dental anatomies by combining digital mouth models and panoramic radiographs. The modeling process is based on using crown surfaces, reconstructed by optical scanning, and root geometries, obtained by adapting anatomical CAD templates over patient specific information extracted from radiographic data. The radiographic process is virtually replicated on crown digital geometries through the Discrete Radon Transform (DRT). The resulting virtual PAN image is used to integrate the actual radiographic data and the digital mouth model. This procedure provides the root references on the 3D digital crown models, which guide a shape adjustment of the dental CAD templates. The entire geometrical models are finally created by merging dental crowns, captured by optical scanning, and root geometries, obtained from the CAD templates

Robust and fully automated segmentation of mandible from CT scans

Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel framework where we define the segmentation as two complementary tasks: recognition and delineation. For recognition, we use random forest regression to localize mandible in 3D. For delineation, we propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation algorithm, operating on the recognized mandible sub-volume. Despite heavy CT artifacts and dental fillings, consisting half of the CT image data in our experiments, we have achieved highly accurate detection and delineation results. Specifically, detection accuracy more than 96% (measured by union of intersection (UoI)), the delineation accuracy of 91% (measured by dice similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff Distance) were found.Comment: 4 pages, 5 figures, IEEE International Symposium on Biomedical Imaging (ISBI) 201

Segmentation of the Oral and Facial Regions from Imaging Modalities with Reduced or No Ionizing Radiation

Ph.DDOCTOR OF PHILOSOPH

Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes

Inferior Alveolar Nerve (IAN) canal detection has been the focus of multiple recent works in dentistry and maxillofacial imaging. Deep learning-based techniques have reached interesting results in this research field, although the small size of 3D maxillofacial datasets has strongly limited the performance of these algorithms. Researchers have been forced to build their own private datasets, thus precluding any opportunity for reproducing results and fairly comparing proposals. This work describes a novel, large, and publicly available mandibular Cone Beam Computed Tomography (CBCT) dataset, with 2D and 3D manual annotations, provided by expert clinicians. Leveraging this dataset and employing deep learning techniques, we are able to improve the state of the art on the 3D mandibular canal segmentation. The source code which allows to exactly reproduce all the reported experiments is released as an open-source project, along with this article

Interactive design of dental implant placements through CAD-CAM technologies: from 3D imaging to additive manufacturing

In the field of oral rehabilitation, the combined use of 3D imaging technologies and computer-guided approaches allows the development of reliable tools to be used in preoperative assessment of implant placement. In particular, the accurate transfer of the virtual planning into the operative field through surgical guides represents the main challenge of modern dental implantology. Guided implant positioning allows surgical and prosthetic approaches with minimal trauma by reducing treatment time and decreasing patient’s discomfort. This paper aims at defining a CAD/CAM framework for the accurate planning of flapless dental implant surgery. The system embraces three major applications: (1) freeform modelling, including 3D tissue reconstruction and 2D/3D anatomy visualization, (2) computer-aided surgical planning and customised template modelling, (3) additive manufacturing of guided surgery template. The tissue modelling approach is based on the integration of two maxillofacial imaging techniques: tomographic scanning and surface optical scanning. A 3D virtual maxillofacial model is created by matching radiographic data, captured by a CBCT scanner, and surface anatomical data, acquired by a structured light scanner. The pre-surgical planning process is carried out and controlled within the CAD application by referring to the integrated anatomical model. A surgical guide is then created by solid modelling and manufactured by additive techniques. Two different clinical cases have been approached by inserting 11 different implants. CAD-based planned fixture placements have been transferred into the clinical field by customised surgical guides, made of a biocompatible resin and equipped with drilling sleeves

Metal Artifact Reduction in Sinograms of Dental Computed Tomography

Use of metal objects such as dental implants, fillings, crowns, screws, nails, prosthesis and plates have increased in dentistry over the past 20 years, which raised a need for new methods for reducing the metal artifacts in medical images. Although there are several algorithms for metal artifact reduction, none of these algorithms are efficient enough to recover the original image free of all artifacts. This thesis presents two approaches for reducing metal artifacts through accurate segmentation of metal objects on dental computed tomography images. First approach was based on construction and tilting of a 3D jaw phantom, aiming to obtain fewer metals on each slice. 3D jaw phantom included the main anatomical structures of a jaw, and multiple metal fillings inserted on the teeth. Each jaw slice on the 3D phantom was tilted in order to mimic the (1) nodding movement, and (2) mouth opening/closing. Second approach was to segment the metals on an experimental dataset, consisting of a Cone-Beam Computed Tomography image, by using different segmentation algorithms. K-means clustering, Otsu’s thresholding method and logarithmic enhancement were used for extracting the metals from a real dental CT slice. Once the metal fillings on the jaw phantom were segmented out from the image, they were compensated by gap filling methods; Discrete Cosine Domain Gap Filling and inpainting. Qualitative and quantitative analyses were carried out for evaluating the performance of implemented segmentation methods. Efficiency of tilting alternatives on the segmentation of metal fillings was compared. In conclusion, jaw opening/closing movement between 24º-30º suggested a significant enhancement in segmentation, thus, metal artifact reduction on the jaw phantom. Inpainting method showed a better performance for both simulated and experimental dataset over the DCT domain gap filling method. Moreover, merging the logarithmic enhancement and inpainting showed superior results over other metal artifact reduction alternatives

Root and canal morphology of the mandibular first molar: A micro-computed tomography-focused observation of literature with illustrative cases. Part 1: External root morphology

The mandibular first molar often requires endodontic intervention, which can be challenging and complex with several variants in the number of canals and roots. Usually, these teeth have a single mesial and distal root, but variants and anomalies have been noted. The incidence of the number of roots can differ between populations. For instance, up to a third of East Asians present with a third root, while the global prevalence is 8.9%. One- and four-rooted first molar teeth are seldom encountered. Over the years different methods have been used to study root and canal morphology, but micro-computed tomography (micro-CT) has provided a non-invasive method to study root and canal morphology in high definition. This paperis the first of two giving an overview of available literature on various aspects of the external and internal root andcanal morphology of the mandibular first permanent molar. The aim is to provide an overview of relevant aspects of the external root morphology of the mandibular first molar in different populations. The content is supported by illustrative micro-CT images and a report on clinical cases where anomalies have been treated

Alveolar Bone Detection from Dental Cone Beam Computed Tomography using YOLOv3-tiny

Cone Beam Computed Tomography (CBCT) is a medical imaging technique widely used in dentistry including dental implant planning. To determine the size of the dental implant, it is necessary to detect the alveolar bone at the implant site. In this study, we propose automatic detection of alveolar bone from CBCT images of teeth using the YOLOv3-tiny method. The YOLOv3-tiny network architecture consists of a seven-layer convolution networks and six max-pooling layers in the Darknet-53 network with two output branch scale predictions. CBCT images of teeth obtained from 4 patients consisted of 800 coronal slices of 2D grayscale images, containing 830 alveolar bone annotations. Before the training process, the ground truth image annotation was made in the form of a bounding box on the alveolar bone object. The detection results of the YOLOv3-tiny model were compared with the detection results of the YOLOv3 and YOLOv2-tiny models. The results of the experiment on 640 training images and 160 testing images showed that YOLOv3-tiny outperformed YOLOv2-tiny with mAP of 98.6% and 96.73%, respectively. Meanwhile, shows the same good result as YOLOv3