217 research outputs found
Automatic segmentation of the left ventricle cavity and myocardium in MRI data
A novel approach for the automatic segmentation has been developed to extract the epi-cardium and endo-cardium boundaries of the left ventricle (lv) of the heart. The developed segmentation scheme takes multi-slice and multi-phase magnetic resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase. The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the (lv) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method
Model-driven segmentation of X-ray left ventricular angiograms
X-ray left ventricular (LV) angiography is an important imaging modality to assess cardiac function. Using a contrast fluid a 2D projection of the heart is obtained. In current clinical practice cardiac function is analyzed by drawing two contours manually: one in the end diastolic (ED) phase and one in the end systolic (ES) phase. From the contours the LV volumes in these phases are calculated and the patient__s ejection fraction is assessed. Drawing these contours manually is a cumbersome and time-consuming task for a medical doctor. Furthermore, manual drawing introduces inter- and intra-observer variabilities. The focus of the research presented in this thesis was to automate the process of contour drawing in X-ray LV angiography. The developed method is based on Active Appearance Models. These statistical models, in which the cardiac shape and the cardiac appearance are modeled, have proven to be able to mimic the drawing behavior of an expert cardiologist. The clinical parameters, as determined by the automated method, showed a similar degree of accuracy as when determined by an expert. Furthermore, the required time for patient analysis was reduced considerably and the inter- and intra-observer variabilities were structurally decreased.UBL - phd migration 201
A novel myocardium segmentation approach based on neutrosophic active contour model
Automatic delineation of the myocardium in echocardiography can assist ra- diologists to diagnosis heart problems. However, it is still challenging to distinguish myocardium from other tissue due to a low signal-to-noise ratio, low contrast, vague boundary, and speckle noise
Fast fully automatic myocardial segmentation in 4D cine cardiac magnetic resonance datasets
Dissertação de mestrado integrado em Engenharia BiomédicaCardiovascular diseases (CVDs) are the leading cause of death in the world, representing
30% of all global deaths. Among others, assessment of the left ventricular (LV) morphology and
global function using non-invasive cardiac imaging is an interesting technique for diagnosis and
treatment follow-up of patients with CVDs. Nowadays, cardiac magnetic resonance (CMR)
imaging is the gold-standard technique for the quantification of LV volumes, mass and ejection
fraction, requiring the delineation of endocardial and epicardial contours of the left ventricle from
cine MR images. In clinical practice, the physicians perform this segmentation manually, being a
tedious, time consuming and unpractical task. Even though several (semi-)automated methods
have been presented for LV CMR segmentation, fast, automatic and optimal boundaries
assessment is still lacking, usually requiring the physician to manually correct the contours.
In the present work, we propose a novel fast fully automatic 3D+time LV segmentation
framework for CMR datasets. The proposed framework presents three conceptual blocks: 1) an
automatic 2D mid-ventricular initialization and segmentation; 2) an automatic stack initialization
followed by a 3D segmentation at the end-diastolic phase; and 3) a tracking procedure to
delineate both endo and epicardial contours throughout the cardiac cycle. In each block, specific
CMR-targeted algorithms are proposed for the different steps required. Hereto, we propose
automatic and feasible initialization procedures. Moreover, we adapt the recent B-spline Explicit
Active Surfaces (BEAS) framework to the properties of CMR image segmentation by integrating
dedicated energy terms and making use of a cylindrical coordinate system that better fits the
topology of CMR data. At last, two tracking methods are presented and compared.
The proposed framework has been validated on 45 4D CMR datasets from a publicly
available database and on a large database from an ongoing multi-center clinical trial with 318
4D datasets. In the technical validation, the framework showed competitive results against the
state-of-the-art methods, presenting leading results in both accuracy and average computational
time in the common database used for comparative purposes. Moreover, the results in the large
scale clinical validation confirmed the high feasibility and robustness of the proposed framework
for accurate LV morphology and global function assessment. In combination with the low
computational burden of the method, the present methodology seems promising to be used in
daily clinical practice.As doenças cardiovasculares (DCVs) são a principal causa de morte no mundo,
representando 30% destas a nível global. Na prática clínica, uma técnica empregue no
diagnóstico de pacientes com DCVs é a avaliação da morfologia e da função global do ventrículo
esquerdo (VE), através de técnicas de imagiologia não-invasivas. Atualmente, a ressonância
magnética cardíaca (RMC) é a modalidade de referência na quantificação dos volumes, massa e
fração de ejeção do VE, exigindo a delimitação dos contornos do endocárdio e epicárdio a partir
de imagens dinâmicas de RMC. Na prática clínica diária, o método preferencial é a segmentação
manual. No entanto, esta é uma tarefa demorada, sujeita a erro humano e pouco prática. Apesar
de até à data diversos métodos (semi)-automáticos terem sido apresentados para a
segmentação do VE em imagens de RMC, ainda não existe um método capaz de avaliar
idealmente os contornos de uma forma automática, rápida e precisa, levando a que geralmente
o médico necessite de corrigir manualmente os contornos.
No presente trabalho é proposta uma nova framework para a segmentação automática
do VE em imagens 3D+tempo de RMC. O algoritmo apresenta três blocos principais: 1) uma
inicialização e segmentação automática 2D num corte medial do ventrículo; 2) uma inicialização
e segmentação tridimensional no volume correspondente ao final da diástole; e 3) um algoritmo
de tracking para obter os contornos ao longo de todo o ciclo cardíaco. Neste sentido, são
propostos procedimentos de inicialização automática com elevada robustez. Mais ainda, é
proposta uma adaptação da recente framework “B-spline Explicit Active Surfaces” (BEAS) com a
integração de uma energia específica para as imagens de RMC e utilizando uma formulação
cilíndrica para tirar partido da topologia destas imagens. Por último, são apresentados e
comparados dois algoritmos de tracking para a obtenção dos contornos ao longo do tempo.
A framework proposta foi validada em 45 datasets de RMC provenientes de uma base de
dados disponível ao público, bem como numa extensa base de dados com 318 datasets para
uma validação clínica. Na avaliação técnica, a framework proposta obteve resultados
competitivos quando comparada com outros métodos do estado da arte, tendo alcançado
resultados de precisão e tempo computacional superiores a estes. Na validação clínica em larga
escala, a framework provou apresentar elevada viabilidade e robustez na avaliação da morfologia
e função global do VE. Em combinação com o baixo custo computacional do algoritmo, a
presente metodologia apresenta uma perspetiva promissora para a sua aplicação na prática
clínica diária
Echocardiography
The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography
Statistical Medial Model dor Cardiac Segmentation and Morphometry
In biomedical image analysis, shape information can be utilized for many purposes. For example, irregular shape features can help identify diseases; shape features can help match different instances of anatomical structures for statistical comparison; and prior knowledge of the mean and possible variation of an anatomical structure\u27s shape can help segment a new example of this structure in noisy, low-contrast images. A good shape representation helps to improve the performance of the above techniques. The overall goal of the proposed research is to develop and evaluate methods for representing shapes of anatomical structures. The medial model is a shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the object\u27s boundary via inverse-skeletonization . This model represents shape compactly, and naturally expresses descriptive global shape features like thickening , bending , and elongation . However, its application in biomedical image analysis has been limited, and it has not yet been applied to the heart, which has a complex shape. In this thesis, I focus on developing efficient methods to construct the medial model, and apply it to solve biomedical image analysis problems. I propose a new 3D medial model which can be efficiently applied to complex shapes. The proposed medial model closely approximates the medial geometry along medial edge curves and medial branching curves by soft-penalty optimization and local correction. I further develop a scheme to perform model-based segmentation using a statistical medial model which incorporates prior shape and appearance information. The proposed medial models are applied to a series of image analysis tasks. The 2D medial model is applied to the corpus callosum which results in an improved alignment of the patterns of commissural connectivity compared to a volumetric registration method. The 3D medial model is used to describe the myocardium of the left and right ventricles, which provides detailed thickness maps characterizing different disease states. The model-based myocardium segmentation scheme is tested in a heterogeneous adult MRI dataset. Our segmentation experiments demonstrate that the statistical medial model can accurately segment the ventricular myocardium and provide useful parameters to characterize heart function
Towards automating cine DENSE MRI image analysis : segmentation, tissue tracking and strain computation
Includes bibliographical references (p. 192-206).Over the past two decades, magnetic resonance imaging (MRI) has developed into a powerful imaging tool for the heart. Imaging cardiac morphology is now commonplace in clinical practice, and a plethora of quantitative techniques have also arisen on the research front. Myocardial tagging is an established quantitative cardiac MRI method that involves magnetically tagging the heart with a set of saturated bands, and monitoring the deformation of these bands as the heart contracts
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