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

CoT-UNet++: A medical image segmentation method based on contextual transformer and dense connection

Accurate depiction of individual teeth from CBCT images is a critical step in the diagnosis of oral diseases, and the traditional methods are very tedious and laborious, so automatic segmentation of individual teeth in CBCT images is important to assist physicians in diagnosis and treatment. TransUNet has achieved success in medical image segmentation tasks, which combines the advantages of Transformer and CNN. However, the skip connection taken by TransUNet leads to unnecessary restrictive fusion and also ignores the rich context between adjacent keys. To solve these problems, this paper proposes a context-transformed TransUNet++ (CoT-UNet++) architecture, which consists of a hybrid encoder, a dense connection, and a decoder. To be specific, a hybrid encoder is first used to obtain the contextual information between adjacent keys by CoTNet and the global context encoded by Transformer. Then the decoder upsamples the encoded features by cascading upsamplers to recover the original resolution. Finally, the multi-scale fusion between the encoded and decoded features at different levels is performed by dense concatenation to obtain more accurate location information. In addition, we employ a weighted loss function consisting of focal, dice, and cross-entropy to reduce the training error and achieve pixel-level optimization. Experimental results demonstrate that the proposed CoT-UNet++ method outperforms the baseline models and can obtain better performance in tooth segmentation

Contributions to the three-dimensional virtual treatment planning of orthognathic surgery

Orientadores: José Mario De Martino, Luis Augusto PasseriTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A tecnologia mais recente à disposição da Cirurgia Ortognática possibilita que o diagnóstico e o planejamento do tratamento das deformidades dentofaciais sejam realizados sob uma representação virtual tridimensional (3D) da cabeça do paciente. Com o propósito de contribuir para o aperfeiçoamento desta tecnologia, o trabalho apresentado nesta tese identificou e tratou quatro problemas. A primeira contribuição consistiu na verificação da validade da hipótese de que a mudança de definição do plano horizontal de Frankfort não produz diferenças de medição clinicamente relevantes quando sob indivíduos cujos crânios são consideravelmente simétricos. Os resultados da análise realizada no contexto deste tese indicam que, ao contrário do que se presumia, a hipótese é falsa. A segunda contribuição consistiu na extensão do método de análise cefalométrica de McNamara para que ele pudesse produzir valores 3D. Ao contrário de outros métodos de análise cefalométrica 3D, a extensão criada produz valores verdadeiramente 3D, não perde as informações do método original e preserva as definições geométricas originais das linhas e planos cefalométricos. A terceira contribuição consistiu a) no estabelecimento de normas cefalométricas para brasileiros adultos de ascendência europeia, a partir de imagens de tomografia computadorizada de feixe cônico, que produz uma imagem craniofacial mais precisa e confiável do que a telerradiografia; e b) na avaliação de dimorfismo sexual, para a identificação de características anatômicas diferenciadas entre homens e mulheres desta população. A quarta e última contribuição consistiu na automatização da principal etapa da tecnologia em questão, na qual o cirurgião executa o reposicionamento dos segmentos ósseos maxilares no crânio. O método criado é capaz de corrigir automaticamente os problemas dentofaciais mais comuns tratados pela Cirurgia Ortognática, que envolvem maloclusão esquelética, assimetria facial e discrepância de maxilares. Todas as contribuições deste trabalho foram publicadas em periódicos internacionais do campo da Odontologia e afinsAbstract: The latest technology available for orthognathic surgery allows the diagnosis and treatment planning of dentofacial deformities based on a three-dimensional (3D) virtual representation of the patient's head. In order to contribute to the improvement of this technology, the work presented in this thesis identified and treated four problems. The first contribution consisted in testing the validity of the hypothesis that changing the definition of the Frankfort horizontal plane does not produce clinically relevant measurement differences for subjects whose skulls are considerably symmetrical. The results of the analysis performed in this thesis indicate that, contrary to what was presumed, the hypothesis is false. The second contribution is an extension of the McNamara's method of cephalometric analysis to produce 3D values. Unlike other methods of 3D cephalometric analysis, the extension produces true 3D values, does not lose information captured by the original method, and preserves the original geometric definitions of the cephalometric lines and planes. The third contribution consisted in a) establishing cephalometric norms for Brazilian adults of European descent, based on images from cone-beam computed tomography, which produce a more accurate and reliable craniofacial image than cephalometric radiography; and b) evaluating sexual dimorphism, for the identification of distinct anatomic features between males and females of this population. The fourth contribution consisted in automating the main stage of the technology in question, in which the surgeon performs the positioning of jaw bone segments in the skull. The created method is able to automatically correct the most common dentofacial problems treated by orthognathic surgery, which involves skeletal malocclusion, facial asymmetry, and jaw discrepancy. The contributions of this work were published in international journals of the field of Dentistry and relatedDoutoradoEngenharia de ComputaçãoDoutor em Engenharia ElétricaCAPE

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