6 research outputs found
Fast left ventricle tracking in CMR images using localized anatomical affine optical flow
"Progress in Biomedical Optics and Imaging, vol. 16, nr. 41"In daily cardiology practice, assessment of left ventricular (LV) global function using non-invasive imaging remains central for the diagnosis and follow-up of patients with cardiovascular diseases. Despite the different methodologies currently accessible for LV segmentation in cardiac magnetic resonance (CMR) images, a fast and complete LV delineation is still limitedly available for routine use. In this study, a localized anatomically constrained affine optical flow method is proposed for fast and automatic LV tracking throughout the full cardiac cycle in short-axis CMR images. Starting from an automatically delineated LV in the end-diastolic frame, the endocardial and epicardial boundaries are propagated by estimating the motion between adjacent cardiac phases using optical flow. In order to reduce the computational burden, the motion is only estimated in an anatomical region of interest around the tracked boundaries and subsequently integrated into a local affine motion model. Such localized estimation enables to capture complex motion patterns, while still being spatially consistent. The method was validated on 45 CMR datasets taken from the 2009 MICCAI LV segmentation challenge. The proposed approach proved to be robust and efficient, with an average distance error of 2.1 mm and a correlation with reference ejection fraction of 0.98 (1.9 ± 4.5%). Moreover, it showed to be fast, taking 5 seconds for the tracking of a full 4D dataset (30 ms per image). Overall, a novel fast, robust and accurate LV tracking methodology was proposed, enabling accurate assessment of relevant global function cardiac indices, such as volumes and ejection fraction.The authors acknowledge funding support from FCT - Fundação para a Ciência e Tecnologia, Portugal, in the scope of the PhD grant SFRH/BD/93443/2013 and the project EXPL/BBB-BMD/2473/2013. D. Barbosa would also like to acknowledge the kind support of the Fundação Luso-Americana para o Desenvolvimento (FLAD), which has funded the travel costs for participation at SPIE Medical Imaging 2015.info:eu-repo/semantics/publishedVersio
An Overview of Techniques for Cardiac Left Ventricle Segmentation on Short-Axis MRI
Nowadays, heart diseases are the leading cause of death. Left ventricle segmentation of a human heart in magnetic resonance images (MRI) is a crucial step in both cardiac diseases diagnostics and heart internal structure reconstruction. It allows estimating such important parameters as ejection faction, left ventricle myocardium mass, stroke volume, etc. In addition, left ventricle segmentation helps to construct the personalized heart computational models in order to conduct the numerical simulations. At present, the fully automated cardiac segmentation methods still do not meet the accuracy requirements. We present an overview of left ventricle segmentation algorithms on short-axis MRI. A wide variety of completely different approaches are used for cardiac segmentation, including machine learning, graph-based methods, deformable models, and low-level heuristics. The current state-of-the-art technique is a combination of deformable models with advanced machine learning methods, such as deep learning or Markov random fields. We expect that approaches based on deep belief networks are the most promising ones because the main training process of networks with this architecture can be performed on the unlabelled data. In order to improve the quality of left ventricle segmentation algorithms, we need more datasets with labelled cardiac MRI data in open access
A framework combining window width-level adjustment and Gaussian filter-based multi-resolution for automatic whole heart segmentation
Heart diseases are prevalent among the general population. These diseases can be diagnosed in their early stages through a quantitative evaluation of cardiac functions. In a typical procedure, heart segmentation is initially performed. Quantitative information is then obtained from a 3D reconstructed image of the heart. However, manual segmentation is time-consuming and prone to inter- and intra-observer variations. As such, automatic methods must be developed to assess cardiac functions quantitatively. In this study, an automatic algorithm for whole heart segmentation was established through window width-level adjustment and Gaussian filter-based multi-resolution methods. The proposed algorithm preprocesses the image by adjusting the window width and the centre to acquire cardiac images with clear anatomical structures. The cardiac image is then decomposed into several resolution layers by using a Gaussian filter to eliminate discontinuity associated with traditional pyramid down-sampling and decomposition. A registration-based segmentation algorithm is applied to the cardiac image. The proposed segmentation algorithm was validated with a clinical dataset of 14 cardiac dual-source computed tomography images. Results show that the proposed methods improve the registration accuracy of the epicardium and the endocardium. The volume of the manual segmentation standard is not significantly different from that of the proposed segmentation and the accuracy of the method reaches almost 1 mm in most areas. Thus, the proposed method can be used to perform a high-precision segmentation of the whole heart
Cardiac motion and deformation estimation in tagged magnetic resonance imaging
Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Electrónica Médica)Cardiovascular diseases are the main cause of death in Europe, with an estimate
of 4.3 million deaths each year. The assessment of the regional wall deformation is a
relevant clinical indicator, and can be used to detect several cardiac lesions. Nowadays,
this study can be performed using several image modalities. In the current thesis, we
focus on tagged Magnetic Resonance imaging (t-MRI) technique. Such technique
allows acquiring images with tags on the myocardium, which deform with the muscle.
The present thesis intends to assess the left ventricle (LV) deformation using
radial and circumferential strain. To compute such strain values, both endo- and
epicardial contours of the LV are required.
As such, a new framework to automatically assess the LV function is proposed.
This framework presents: (i) an automatic segmentation technique, based on a tag
suppression strategy followed by an active contour segmentation method, and (ii) a
tracking approach to extract myocardial deformation, based on a non-rigid registration
method. The automatic segmentation uses the B-spline Explicit Active Surface
framework, which was previously applied in ultra-sound and cine-MRI images. In both
cases, a real-time and accurate contour was achieved. Regarding the registration step,
starting from a state-of-art approach, termed sequential 2D, we suggest a new method
(termed sequential 2D+t), where the temporal information is included on the model.
The tracking methods were first tested on synthetic data to study the registration
parameters influence. Furthermore, the proposed and original methods were applied on
porcine data with myocardial ischemia. Both methods were able to detect dysfunctional
regions. A comparison between the strain curve in the sequential 2D and sequential
2D+t strategies was also shown. As conclusion, a smoothing effect in the strain curve
was detected in the sequential 2D+t strategy. The validation of the segmentation
approach uses a human dataset. A comparison between the manual contour and the
proposed segmentation method results was performed. The results, suggest that
proposed method has an acceptable performance, removing the tedious task related with
manual segmentation and the intra-observer variability. Finally, a comparison between
the proposed framework and the currently available commercial software was
performed. The commercial software results were obtained from core-lab analysis. An
acceptable result (r = 0.601) was achieved when comparing the strain peak values.
Importantly, the proposed framework appears to present a more acceptable result.As doenças cardiovasculares são a principal causa de morte na Europa, com
aproximadamente 4.7 milhões de mortes por ano. A avaliação da deformação do
miocárdio a um nível local é um importante indicador clínico e pode ser usado para a
deteção de lesões cardíacas. Este estudo é normalmente realizado usando várias
modalidades de imagem médica. Nesta tese, a Resonância Magnética (RM) marcada foi
a técnica selecionada. Estas imagens têm marcadores no músculo cardíaco, os quais se
deformam com o miocárdio e podem ser usados para o estudo da deformação cardíaca.
Nesta tese, pretende-se estudar a deformação radial e circunferencial do
ventrículo esquerdo (VE). Assim, um contorno do endo- e epicárdio no VE é essencial.
Desta forma, uma ferramenta para o estudo da deformação do VE foi
desenvolvida. Esta possui: (i) um método de segmentação automático, usando uma
estratégia de supressão dos marcadores, seguido de uma segmentação c um contorno
ativo, e (ii) um método de tracking para determinação da deformação cardíaca, baseado
em registo não rígido. A segmentação automática utiliza a ferramenta B-spline Explicit
Active Surface, que foi previamente aplicada em imagens de ultrassons e cine-RM. Em
ambos os casos, uma segmentação em tempo real e com elevada exatidão foi alcançada.
Vários esquemas de registo foram apresentados. Neste ponto, começando com uma
técnica do estado da arte (designada de sequencial 2D), uma nova metodologia foi
proposta (sequencial 2D+t), onde a informação temporal é incorporada no modelo.
De forma a analisar a influência dos parâmetros do registo, estes foram
estudados num dataset sintético. De seguida, os diferentes esquemas de registo foram
testados num dataset suíno com isquemia. Ambos os métodos foram capazes de detetar
as regiões disfuncionais. De igual forma, utilizando as curvas de deformação obtidas
para cada um dos métodos propostos, foi possível observar uma suavização na direção
temporal para o método sequencial 2D+t. Relativamente à segmentação, esta foi
validada com um dataset humano. Um contorno manual foi comparado com o obtido
pelo método proposto. Os resultados sugerem que a nova estratégia é aceitável, sendo
mais rápida do que a realização de um contorno manual e eliminando a variabilidade
entre observadores. Por fim, realizou-se uma comparação entre a ferramenta proposta e
um software comercial (com análise de core-lab). A comparação entre os valores de
pico da deformação exibe uma correlação plausível (r=0.601). Contudo, é importante
notar, que a nova ferramenta tende a apresentar um resultado mais aceitável
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