Variations in the Cross-Sectional Shape of the Apical Thirds of the Root Canals in Maxillary and Mandibular Teeth

Abstract Objective: To determine the prevalence of cross-sectional variations in the apical thirds of the root canals in maxillary and mandibular teeth. Material and Methods: Eighty tooth samples (maxillary second premolar, maxillary first molar, and mandibular first molar) were scanned using micro-computed tomography. The apical third area of each root canal was sectioned and the maximum and minimum diameters were calculated from the mesiodistal and buccolingual diameters. The shapes were categorized as a round, oval, long oval, and flat based on the ratios obtained. Results: The most common shape of the apical third of the root canals in the maxillary second premolars was oval (66.7%), followed by long oval (24.6%), flat (7%), and round (1.7%). The corresponding values in the mesiobuccal root of the maxillary first molars were oval (68.2%), long oval (22.7%), flat (9.1%), 94.1% of the distobuccal roots were oval, while the remaining were long oval (5.9%). All the palatal root canals were oval. In the mesiobuccal root of the mandibular first molars, 47.4% were long oval in shape, followed by 36.8% oval and 15.8% flat canals. All the mesiolingual root canals were oval, whereas, in the distal root, 68.4% were oval, 21.1% long oval, and 10.5% were flat. Conclusion: The oval shape was most commonly observed in the majority of the root canals. Knowledge about the apical anatomy of the root can help the operator improve the root canal treatment's success.

Modelo de contorno ativo crisp: nova técnica de segmentação dos pulmões em imagens de TC / Active contour modes crisp: new technique for segmentation the lungs in CT images

Este trabalho propõe um novo método de contornos ativos (MCA), chamado de MCA Crisp, e o avalia na segmentação dos pulmões em imagens de Tomografia Computadorizada (TC). O MCA consiste em traçar uma curva inicial em torno ou dentro de um objeto de interesse. Esta curva se deforma, conforme algumas energias que atuam sobre a mesma, deslocando-a até as bordas do objeto. Este processo é realizado por iterações sucessivas de minimização de uma dada energia, associada à curva. Aplicando MCAs descritos na literatura na segmentação dos pulmões em imagens de TC, constatam-se limitações. Neste contexto, propõe‑-se o MCA Crisp para suprir tais limitações, propondo uma inicialização automática e uma nova energia externa baseada em regras e nas densidades radiológicas pulmonares. Realiza-se uma comparação entre os MCAs Tradicional, Balão, GVF e o método proposto para demonstrar a superioridade do novo método. Em seguida, para validar o MCA Crisp realiza-se uma análise qualitativa junto a um médico especialista na área de Pneumologia do Hospital Universitário Walter Cantídio da Universidade Federal do Ceará. Nesta análise, são utilizados 100  pulmões em imagens de TC. A eficiência da segmentação foi avaliada em  5 categorias, obtendo os seguintes resultados:   73% ótimas, sem falhas, 20% aceitáveis, com pequenos erros, 7% razoáveis, com erros grosseiros e  0% ruim, segmentando apenas uma pequena parte do pulmão, e  0% péssima, obtendo uma segmentação totalmente errada. Conclui-se que o MCA Crisp é um método útil para segmentar os pulmões em imagens de TC e com potencial para integrar sistemas que auxiliem o diagnóstico médico.

This paper proposes a new Active Contour Model (ACM), called ACM Crisp, and evaluates the segmentation of lungs in computed tomography (CT) images. An ACM draws a curve around or within the object of interest. This curve changes its shape, when some energy acts on it and moves towards the edges of the object. This process is performed by successive iterations of minimization of a given energy, associated with the curve. The ACMs described in the literature have limitations when used for segmentations of CT lung images. The ACM Crisp model overcomes these limitations, since it proposes automatic initiation and new external energy based on rules and radiological pulmonary densities. The paper compares other ACMs with the proposed method, which is shown to be superior. In order to validate the algorithm a medical expert in the field of Pulmonology of the Walter Cantídio University Hospital from the Federal University of Ceará carried out a qualitative analysis. In these analyses  100 CT lung images were used. The segmentation efficiency was evaluated into  5 categories with the following results for the ACM Crisp: 73% excellent, without errors, 20% acceptable, with small errors, and  7% reasonable, with large errors, 0% poor, covering only a small part of the lung, and  0% very bad, making a totally incorrect segmentation. In conclusion the ACM Crisp is considered a useful algorithm to segment CT lung images, and with potential to integrate medical diagnosis systems.