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

Digital three-dimensional visualization of intrabony periodontal defects for regenerative surgical treatment planning

BACKGROUND: In the regenerative treatment of intrabony periodontal defects, surgical strategies are primarily determined by defect morphologies. In certain cases, however, direct clinical measurements and intraoral radiographs do not provide sufficient information on defect morphologies. Therefore, the application of cone-beam computed tomography (CBCT) has been proposed in specific cases. 3D virtual models reconstructed with automatic thresholding algorithms have already been used for diagnostic purposes. The aim of this study was to utilize 3D virtual models, generated with a semi-automatic segmentation method, for the treatment planning of minimally invasive periodontal surgeries and to evaluate the accuracy of the virtual models, by comparing digital measurements to direct intrasurgical measurements. METHODS: Four patients with a total of six intrabony periodontal defects were enrolled in the present study. Two months following initial periodontal treatment, a CBCT scan was taken. The novel semi-automatic segmentation method was performed in an open-source medical image processing software (3D Slicer) to acquire virtual 3D models of alveolar and dental structures. Intrasurgical and digital measurements were taken, and results were compared to validate the accuracy of the digital models. Defect characteristics were determined prior to surgery with conventional diagnostic methods and 3D virtual models. Diagnostic assessments were compared to the actual defect morphology during surgery. RESULTS: Differences between intrasurgical and digital measurements in depth and width of intrabony components of periodontal defects averaged 0.31 ± 0.21 mm and 0.41 ± 0.44 mm, respectively. In five out of six cases, defect characteristics could not be assessed precisely with direct clinical measurements and intraoral radiographs. 3D models generated with the presented semi-automatic segmentation method depicted the defect characteristics correctly in all six cases. CONCLUSION: It can be concluded that 3D virtual models acquired with the described semi-automatic segmentation method provide accurate information on intrabony periodontal defect morphologies, thus influencing the treatment strategy. Within the limitations of this study, models were found to be accurate; however, further investigation with a standardized validation process on a large number of participants has to be conducted

Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation

Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the inter-overlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.Comment: 10 pages, 10 figure

Segmentation automatique des dents en imagerie maxillo-faciale Cone Beam CT

La planification de traitement et la simulation chirurgicale en médecine dentaire nécessite l’obtention de modèles 3D personnalisés du système dentaire du patient. La représentation tridimensionnelle de ces structures, et plus particulièrement des dents, fournit une compréhension poussée des relations dento-maxillo-faciales, ce qui permet au clinicien de sélectionner un plan de traitement optimisé. L’obtention de ces modèles personnalisés se faisant à l’aide de modalités d’imagerie 3D, la segmentation des organes d’intérêt est une étape essentielle à l’obtention de modèles fiables et précis. Compte tenu de la complexité morphologique des dents, mais aussi des contraintes inhérentes à l’utilisation du Cone Beam CT, une automatisation du processus de segmentation est certainement souhaitable. En ce sens, l’objectif de ce travail implique la mise en place d’une méthode entièrement automatique de segmentation individuelle des dents à partir d’images maxillo-faciales CBCT. Le processus de segmentation se divise en deux grandes étapes principales. Dans un premier temps, des sous-régions du volume 3D original sont extraites, afin de circonscrire chacune des dents au sein de volumes restreints. Cette étape se base sur l’identification automatique de repères anatomiques propres au complexe maxillo-facial. Entre autres, l’identification de la courbe décrivant la forme de l’arcade dentaire ainsi que le positionnement de plans séparant les dents sur l’arcade guident l’extraction de ces sous-volumes. Ces derniers sont ensuite utilisés de manière indépendante dans un algorithme de détection de la pulpe dentaire basé sur la reconstruction morphologique. La forme de la pulpe permet, dans un second temps, de tracer des contours significatifs de la dent via la propagation d’un front sous contraintes de gradient. Ces contours instancient un processus de segmentation par marche aléatoire afin de fournir un modèle pré-personnalisé de chaque dent. Le modèle surfacique subit ensuite une déformation par optimisation laplacienne, afin d’épouser correctement les frontières de la dent. Les modèles 3D résultants constituent une représentation fiable et précise des structures dentaires du patient. Ces modèles ont été validés à l’aide d’une base de données contenant 88 segmentations de référence, toutes produites par un expert. La performance globale de la segmentation se traduit par un indice de Dice (DICE) de 95,20±1,07 %, une différence relative de volume (RVD) de 2,57±3,21 % et une distance surfacique moyenne-symétrique (ASD) de 0,16±0,04 mm. Les résultats de ce travail démontrent que la méthode fournit automatiquement des segmentations multi-organes précises à partir d’un examen 3D de la mâchoire du patient