Jaw tissues segmentation in dental 3D CT images using fuzzy-connectedness and morphological processing

The success of oral surgery is subject to accurate advanced planning. In order to properly plan for dental surgery or a suitable implant placement, it is necessary an accurate segmentation of the jaw tissues: the teeth, the cortical bone, the trabecular core and over all, the inferior alveolar nerve. This manuscript presents a new automatic method that is based on fuzzy connectedness object extraction and mathematical morphology processing. The method uses computed tomography data to extract different views of the jaw: a pseudo-orthopantomographic view to estimate the path of the nerve and cross-sectional views to segment the jaw tissues. The method has been tested in a groundtruth set consisting of more than 9000 cross-sections from 20 different patients and has been evaluated using four similarity indicators (the Jaccard index, Dice's coefficient, point-to-point and point-to-curve distances), achieving promising results in all of them (0.726 ± 0.031, 0.840 ± 0.019, 0.144 ± 0.023 mm and 0.163 ± 0.025 mm, respectively). The method has proven to be significantly automated and accurate, with errors around 5% (of the diameter of the nerve), and is easily integrable in current dental planning systems. © 2012 Elsevier Ireland Ltd.This work has been supported by the project MIRACLE (DPI2007-66782-C03-01-AR07) of Spanish Ministerio de Educacion y Ciencia.Llorens Rodríguez, R.; Naranjo Ornedo, V.; López-Mir, F.; Alcañiz Raya, ML. (2012). Jaw tissues segmentation in dental 3D CT images using fuzzy-connectedness and morphological processing. Computer Methods and Programs in Biomedicine. 108(2):832-843. https://doi.org/10.1016/j.cmpb.2012.05.014832843108

Improving statistics for hybrid segmentation of high-resolution multichannel images

High-resolution multichannel textures are difficult to characterize with simple statistics and the high level of detail makes the selection of a particular contour using classical gradient-based methods not effective. We have developed a hybrid method that combines fuzzy connectedness and Voronoi diagram classification for the segmentation of color and multichannel objects. The multi-step classification process relies on homogeneity measures derived from moment statistics and histogram information. These color features have been optimized to best combine individual channel information in the classification process. The segmentation initialization requires only a set of interior and exterior seed points, minimizing user intervention and the influence of the initialization on the overall quality of the results. The method was tested on volumes from the Visible Human and on brain multi-protocol MRI data sets. The hybrid segmentation produced robust, rapid and finely detailed contours with good visual accuracy. The addition of quantized statistics and color histogram distances as classification features improved the robustness of the method with regards to initialization when compared to our original implementation

Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms

The authors analyze the design of a method for automatized evaluation of parameters in orthopantomographic images capturing pathological tissues developed in human jaw bones. The main problem affecting the applied medical diagnostic procedures consists in low repeatability of the performed evaluation. This condition is caused by two aspects, namely subjective approach of the involved medical specialists and the related exclusion of image processing instruments from the evaluation scheme. The paper contains a description of the utilized database containing images of cystic jaw bones; this description is further complemented with appropriate schematic repre¬sentation. Moreover, the authors present the results of fast automatized segmentation realized via the live-wire method and compare the obtained data with the results provided by other segmentation techniques. The shape parameters and the basic statistical quantities related to the distribution of intensities in the segmented areas are selected. The evaluation results are provided in the final section of the study; the authors correlate these values with the subjective assessment carried out by radiologists. Interestingly, the paper also comprises a discussion presenting the possibility of using selected parameters or their combinations to execute automatic classification of cysts and osteonecrosis. In this context, a comparison of various classifiers is performed, including the Decision Tree, Naive Bayes, Neural Network, k-NN, SVM, and LDA classifica¬tion tools. Within this comparison, the highest degree of accuracy (85% on the average) can be attributed to the Decision Tree, Naive Bayes, and Neural Network classifier

Odontology & artificial intelligence

Neste trabalho avaliam-se os três fatores que fizeram da inteligência artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorítmicos. Esta revisão da literatura avaliou todos os artigos publicados na PubMed até Abril de 2019 sobre inteligência artificial e odontologia. Ajudado com inteligência artificial, este artigo analisou 1511 artigos. Uma árvore de decisão (If/Then) foi executada para selecionar os artigos mais relevantes (217), e um algoritmo de cluster k-means para resumir e identificar oportunidades de inovação. O autor discute os artigos mais interessantes revistos e compara o que foi feito em inovação durante o International Dentistry Show, 2019 em Colónia. Concluiu, assim, de forma crítica que há uma lacuna entre tecnologia e aplicação clínica desta, sendo que a inteligência artificial fornecida pela indústria de hoje pode ser considerada um atraso para o clínico de amanhã, indicando-se um possível rumo para a aplicação clínica da inteligência artificial.There are three factors that have made artificial intelligence (AI) an essential technology today: the computer performance, Big Data and algorithmic advances. This study reviews the literature on AI and Odontology based on articles retrieved from PubMed. With the help of AI, this article analyses a large number of articles (a total of 1511). A decision tree (If/Then) was run to select the 217 most relevant articles-. Ak-means cluster algorithm was then used to summarize and identify innovation opportunities. The author discusses the most interesting articles on AI research and compares them to the innovation presented during the International Dentistry Show 2019 in Cologne. Three technologies available now are evaluated and three suggested options are been developed. The author concludes that AI provided by the industry today is a hold-up for the praticioner of tomorrow. The author gives his opinion on how to use AI for the profit of patients

Desarrollo de un Módulo de Tratamiento de Imagen para Sistemas de Imagen Dental

Este trabajo resume los desarrollos llevados a cabo sobre reducción de artefactos metálicos y de segmentación de tejidos mandibulares que salen al paso de las limitaciones de los sistemas de imagen dental actuales. Los métodos propuestos han sido evaluados analíticamente obteniendo resultados satisfactorios respecto al estado del arte actual, hecho que ha dado lugar a un número considerable de publicaciones científicas.Lloréns Rodríguez, R. (2011). Desarrollo de un Módulo de Tratamiento de Imagen para Sistemas de Imagen Dental. http://hdl.handle.net/10251/28047.Archivo delegad

Synergistic Visualization And Quantitative Analysis Of Volumetric Medical Images

The medical diagnosis process starts with an interview with the patient, and continues with the physical exam. In practice, the medical professional may require additional screenings to precisely diagnose. Medical imaging is one of the most frequently used non-invasive screening methods to acquire insight of human body. Medical imaging is not only essential for accurate diagnosis, but also it can enable early prevention. Medical data visualization refers to projecting the medical data into a human understandable format at mediums such as 2D or head-mounted displays without causing any interpretation which may lead to clinical intervention. In contrast to the medical visualization, quantification refers to extracting the information in the medical scan to enable the clinicians to make fast and accurate decisions. Despite the extraordinary process both in medical visualization and quantitative radiology, efforts to improve these two complementary fields are often performed independently and synergistic combination is under-studied. Existing image-based software platforms mostly fail to be used in routine clinics due to lack of a unified strategy that guides clinicians both visually and quan- titatively. Hence, there is an urgent need for a bridge connecting the medical visualization and automatic quantification algorithms in the same software platform. In this thesis, we aim to fill this research gap by visualizing medical images interactively from anywhere, and performing a fast, accurate and fully-automatic quantification of the medical imaging data. To end this, we propose several innovative and novel methods. Specifically, we solve the following sub-problems of the ul- timate goal: (1) direct web-based out-of-core volume rendering, (2) robust, accurate, and efficient learning based algorithms to segment highly pathological medical data, (3) automatic landmark- ing for aiding diagnosis and surgical planning and (4) novel artificial intelligence algorithms to determine the sufficient and necessary data to derive large-scale problems

A 3D environment for surgical planning and simulation

The use of Computed Tomography (CT) images and their three-dimensional (3D) reconstruction has spread in the last decade for implantology and surgery. A common use of acquired CT datasets is to be handled by dedicated software that provide a work context to accomplish preoperative planning upon. These software are able to exploit image processing techniques and computer graphics to provide fundamental information needed to work in safety, in order to minimize the surgeon possible error during the surgical operation. However, most of them carry on lacks and flaws, that compromise the precision and additional safety that their use should provide. The research accomplished during my PhD career has concerned the development of an optimized software for surgical preoperative planning. With this purpose, the state of the art has been analyzed, and main deficiencies have been identified. Then, in order to produce practical solutions, those lacks and defects have been contextualized in a medical field in particular: it has been opted for oral implantology, due to the available support of a pool of implantologists. It has emerged that most software systems for oral implantology, that are based on a multi-view approach, often accompanied with a 3D rendered model, are affected by the following problems: unreliability of measurements computed upon misleading views (panoramic one), as well as a not optimized use of the 3D environment, significant planning errors implied by the software work context (incorrect cross-sectional planes), and absence of automatic recognition of fundamental anatomies (as the mandibular canal). Thus, it has been defined a fully 3D approach, and a planning software system in particular, where image processing and computer graphic techniques have been used to create a smooth and user-friendly completely-3D environment to work upon for oral implant planning and simulation. Interpolation of the axial slices is used to produce a continuous radiographic volume and to get an isotropic voxel, in order to achieve a correct work context. Freedom of choosing, arbitrarily, during the planning phase, the best cross-sectional plane for achieving correct measurements is obtained through interpolation and texture generation. Correct orientation of the planned implants is also easily computed, by exploiting a radiological mask with radio-opaque markers, worn by the patient during the CT scan, and reconstructing the cross-sectional images along the preferred directions. The mandibular canal is automatically recognised through an adaptive surface-extracting statistical-segmentation based algorithm developed on purpose. Then, aiming at completing the overall approach, interfacing between the software and an anthropomorphic robot, in order to being able to transfer the planning on a surgical guide, has been achieved through proper coordinates change and exploiting a physical reference frame in the radiological stent. Finally, every software feature has been evaluated and validated, statistically or clinically, and it has resulted that the precision achieved outperforms the one in literature

Automatic Segmentation of the Mandible for Three-Dimensional Virtual Surgical Planning

Three-dimensional (3D) medical imaging techniques have a fundamental role in the field of oral and maxillofacial surgery (OMFS). 3D images are used to guide diagnosis, assess the severity of disease, for pre-operative planning, per-operative guidance and virtual surgical planning (VSP). In the field of oral cancer, where surgical resection requiring the partial removal of the mandible is a common treatment, resection surgery is often based on 3D VSP to accurately design a resection plan around tumor margins. In orthognathic surgery and dental implant surgery, 3D VSP is also extensively used to precisely guide mandibular surgery. Image segmentation from the radiography images of the head and neck, which is a process to create a 3D volume of the target tissue, is a useful tool to visualize the mandible and quantify geometric parameters. Studies have shown that 3D VSP requires accurate segmentation of the mandible, which is currently performed by medical technicians. Mandible segmentation was usually done manually, which is a time-consuming and poorly reproducible process. This thesis presents four algorithms for mandible segmentation from CT and CBCT and contributes to some novel ideas for the development of automatic mandible segmentation for 3D VSP. We implement the segmentation approaches on head and neck CT/CBCT datasets and then evaluate the performance. Experimental results show that our proposed approaches for mandible segmentation in CT/CBCT datasets exhibit high accuracy

Liver segmentation in MRI: a fully automatic method based on stochastic partitions

There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 +/- 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI. (C) 2014 Elsevier Ireland Ltd. All rights reserved.This work has been supported by the MITYC under the project NaRALap (ref. TSI-020100-2009-189), partially by the CDTI under the project ONCOTIC (IDI-20101153), by Ministerio de Educacion y Ciencia Spain, Project Game Teen (TIN2010-20187) projects Consolider-C (SEJ2006-14301/PSIC), "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII" and Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008-157). We would like to express our gratitude to the Hospital Clinica Benidorm, for providing the MR datasets and to the radiologist team of Inscanner for the manual segmentation of the MR images.López-Mir, F.; Naranjo Ornedo, V.; Angulo, J.; Alcañiz Raya, ML.; Luna, L. (2014). Liver segmentation in MRI: a fully automatic method based on stochastic partitions. Computer Methods and Programs in Biomedicine. 114(1):11-28. https://doi.org/10.1016/j.cmpb.2013.12.022S1128114