No description available.

No description available.

Com os avanços tecnológicos das últimas décadas, a ecocardiografia surgiu como uma alternativa de diagnóstico por imagem de relativo baixo custo, que não faz uso de energia ionizante ou radioativa. Recentemente, o advento dos agentes de contraste por microbolhas e dos transdutores matriciais tornou possível a visualização tridimensional da anatomia das artérias coronárias. Neste projeto, é proposta a avaliação de métodos de segmentação capazes de visibilizar as artérias coronárias epicárdicas em Imagens de ecocardiografias tridimensionais com contraste de microbolhas. Esse é o primeiro passo para o desenvolvimento de ferramentas computacionais eficazes e eficientes na assistência não invasiva ao acompanhamento do quadro clínico de pacientes, do diagnóstico ao pós-operatório. Propõe-se, uma metodologia que facilite o acesso às coronárias a partir de imagens de ecocardiografia tridimensionais com aplicação de contraste por microbolhas. Dentre as metodologias estudadas, as técnicas baseadas na teoria Fuzzy Connectedness (FC) foram identificadas como as mais promissoras. Estudou-se, portanto, seis abordagens baseadas nessa teoria, três delas são descritas na literatura (Generalized FC GFC; Relative FC RFC; Dynamic Weighted FC DyWFC) e três proposições originais (Area of Search FC ASFC; Ultrasound-k FC USFC; Guided FC GuFC). Para avaliar a acurácia desses algoritmos, confeccionou-se um conjunto de imagens simuladas, composto por 360 imagens, e selecionou-se um conjunto de imagens de exames reais, composto de 10 imagens reais de pacientes com quadro de Cardiomiopatia Hipertrópica. Para as imagens simuladas, os métodos da literatura alcançaram acurácia de 85,5% para GFC, 89,5% para RFC e 92,0% para DyWFC. Enquanto isso, os métodos propostos alcançaram acurácia de 88,9% para ASFC, 91,7 % para USkFC e 95,2% para GuFC. Para as imagens reais, os métodos convergiram para uma segmentação satisfatória quanto à usabilidade na clínica médica. Esses resultados demonstraram, ainda, o melhor desempenho do método proposto GuFC ante os demais. Dessa forma, ele se torna um candidato para ingressar na etapa de segmentação de uma ferramenta computacional para visibilização das coronárias epicárdicas no futuro / With the technological advances of recent decades, echocardiography has emerged as a relatively low cost imaging diagnostic alternative, that does not use ionizing or radioactive energy. Lately, the advent of microbubble-based contrast agents and array transducers turned possible the visualization of three-dimensional coronary arteries anatomy. The present project proposes to evaluate segmentation methods able to deal with the visualization of the epicardial coronary arteries in microbubble-based three-dimensional echocardiography images. This is the first step towards the development of effective and efficient computational tools for diagnosis and prognosis assistance of cardiac pacient. We propose a methodology to facilitate the access to epicardial coronary arteries in tridimensional echocardiographic images. Among the studied approaches, Fuzzy Connectednessbased segmentation methods were identified as being the most promising. We studied six approaches based on this theory, three of them are described in the literature (Generalized FC GFC; Relative FC RFC; Dynamic Weighted FC DyWFC) and three original contributions (Area of Search FC ASFC; Ultrasound-k FC USFC; Guided FC GuFC). To evaluate the accuracy of these algorithms, a set composed of 360 simulated images were created. We also selected a set of 10 real images, composed of hypertrophic cardiomyopathy patients. For simulated images set, the methods of literature achieved accuracy of 85.5% for GFC, 89,5% for RFC and 92,0% for DyWFC, meanwhile, the proposed method achieved accuracy of 88.9% for ASFC, 91,7 % for USkFC and 95,2% for GuFC. Using the real images set, the methods converged to good results for clinical purposes. These results demonstrate that the proposed method GuFC has shown a better performance than the others, becoming a candidate to the segmentation step in a computational tool for coronary arteries visualization in the future

Com os avanços tecnológicos das últimas décadas, a ecocardiografia surgiu como uma alternativa de diagnóstico por imagem de relativo baixo custo, que não faz uso de energia ionizante ou radioativa. Recentemente, o advento dos agentes de contraste por microbolhas e dos transdutores matriciais tornou possível a visualização tridimensional da anatomia das artérias coronárias. Neste projeto, é proposta a avaliação de métodos de segmentação capazes de visibilizar as artérias coronárias epicárdicas em Imagens de ecocardiografias tridimensionais com contraste de microbolhas. Esse é o primeiro passo para o desenvolvimento de ferramentas computacionais eficazes e eficientes na assistência não invasiva ao acompanhamento do quadro clínico de pacientes, do diagnóstico ao pós-operatório. Propõe-se, uma metodologia que facilite o acesso às coronárias a partir de imagens de ecocardiografia tridimensionais com aplicação de contraste por microbolhas. Dentre as metodologias estudadas, as técnicas baseadas na teoria Fuzzy Connectedness (FC) foram identificadas como as mais promissoras. Estudou-se, portanto, seis abordagens baseadas nessa teoria, três delas são descritas na literatura (Generalized FC GFC; Relative FC RFC; Dynamic Weighted FC DyWFC) e três proposições originais (Area of Search FC ASFC; Ultrasound-k FC USFC; Guided FC GuFC). Para avaliar a acurácia desses algoritmos, confeccionou-se um conjunto de imagens simuladas, composto por 360 imagens, e selecionou-se um conjunto de imagens de exames reais, composto de 10 imagens reais de pacientes com quadro de Cardiomiopatia Hipertrópica. Para as imagens simuladas, os métodos da literatura alcançaram acurácia de 85,5% para GFC, 89,5% para RFC e 92,0% para DyWFC. Enquanto isso, os métodos propostos alcançaram acurácia de 88,9% para ASFC, 91,7 % para USkFC e 95,2% para GuFC. Para as imagens reais, os métodos convergiram para uma segmentação satisfatória quanto à usabilidade na clínica médica. Esses resultados demonstraram, ainda, o melhor desempenho do método proposto GuFC ante os demais. Dessa forma, ele se torna um candidato para ingressar na etapa de segmentação de uma ferramenta computacional para visibilização das coronárias epicárdicas no futuro / With the technological advances of recent decades, echocardiography has emerged as a relatively low cost imaging diagnostic alternative, that does not use ionizing or radioactive energy. Lately, the advent of microbubble-based contrast agents and array transducers turned possible the visualization of three-dimensional coronary arteries anatomy. The present project proposes to evaluate segmentation methods able to deal with the visualization of the epicardial coronary arteries in microbubble-based three-dimensional echocardiography images. This is the first step towards the development of effective and efficient computational tools for diagnosis and prognosis assistance of cardiac pacient. We propose a methodology to facilitate the access to epicardial coronary arteries in tridimensional echocardiographic images. Among the studied approaches, Fuzzy Connectednessbased segmentation methods were identified as being the most promising. We studied six approaches based on this theory, three of them are described in the literature (Generalized FC GFC; Relative FC RFC; Dynamic Weighted FC DyWFC) and three original contributions (Area of Search FC ASFC; Ultrasound-k FC USFC; Guided FC GuFC). To evaluate the accuracy of these algorithms, a set composed of 360 simulated images were created. We also selected a set of 10 real images, composed of hypertrophic cardiomyopathy patients. For simulated images set, the methods of literature achieved accuracy of 85.5% for GFC, 89,5% for RFC and 92,0% for DyWFC, meanwhile, the proposed method achieved accuracy of 88.9% for ASFC, 91,7 % for USkFC and 95,2% for GuFC. Using the real images set, the methods converged to good results for clinical purposes. These results demonstrate that the proposed method GuFC has shown a better performance than the others, becoming a candidate to the segmentation step in a computational tool for coronary arteries visualization in the future

No description available.

Heart disease is the major cause of death in the developed world. Due to its fast, portable, low-cost and harmless way of imaging the heart, echocardiography has become the most frequent tool for diagnosis of cardiac function in clinical routine. However, visual assessment of heart function from echocardiography is challenging, highly operatordependant and is subject to intra- and inter observer errors. Therefore, development of automated methods for echocardiography analysis is important towards accurate assessment of cardiac function. In this thesis we develop new ways to model echocardiography data using Bayesian machine learning methods and concern three problems: (i) wall motion analysis in 2D stress echocardiography, (ii) segmentation of the myocardium in 3D echocardiography, and (iii) standard views extraction from 3D echocardiography. Firstly, we propose and compare four discriminative methods for feature extraction and wall motion classification of 2D stress echocardiography (images of the heart taken at rest and after exercise or pharmalogical stress). The four methods are based on (i) Support Vector Machines, (ii) Relevance Vector Machines, (iii) Lasso algorithm and Regularised Least Squares, (iv) Elastic Net regularisation and Regularised Least Squares. Although all the methods are shown to have superior performance to the state-of-the-art, one conclusion is that good segmentation of the myocardium in echocardiography is key for accurate assessment of cardiac wall motion. We investigate the application of one of the most promising current machine learning techniques, called Decision Random Forests, to segment the myocardium from 3D echocardiograms. We demonstrate that more reliable and ultrasound specific descriptors are needed in order to achieve the best results. Specifically, we introduce two sets of new features to improve the segmentation results: (i) LoCo and GloCo features with a local and a global shape constraint on coupled endoand epicardial boundaries, and (ii) FA features, which use the Feature Asymmetry measure to highlight step-like edges in echocardiographic images. We also reinforce the traditional features such as Haar and Rectangular features by aligning 3D echocardiograms. For that we develop a new registration technique, which is based on aligning centre lines of the left ventricles. We show that with alignment performance is boosted by approximately 15%. Finally, a novel approach to detect planes in 3D images using regression voting is proposed. To the best of our knowledge we are the first to use a one-step regression approach for the task of plane detection in 3D images. We investigate the application to standard views extraction from 3D echocardiography to facilitate efficient clinical inspection of cardiac abnormalities and diseases. We further develop a new method, called the Class- Specific Regression Forest, where class label information is incorporating into the training phase to reinforce the learning from semantically relevant to the problem classes. During testing the votes from irrelevant classes are excluded from voting to maximise the confidence of output predictors. We demonstrate that the Class-Specific Regression Random Forest outperforms the classic Regression Random Forest and produces results comparable to the manual annotations.