Automated Segmentation of Left and Right Ventricles in MRI and Classification of the Myocarfium Abnormalities

A fundamental step in diagnosis of cardiovascular diseases, automated left and right ventricle (LV and RV) segmentation in cardiac magnetic resonance images (MRI) is still acknowledged to be a difficult problem. Although algorithms for LV segmentation do exist, they require either extensive training or intensive user inputs. RV segmentation in MRI has yet to be solved and is still acknowledged a completely unsolved problem because its shape is not symmetric and circular, its deformations are complex and varies extensively over the cardiac phases, and it includes papillary muscles. In this thesis, I investigate fast detection of the LV endo- and epi-cardium surfaces (3D) and contours (2D) in cardiac MRI via convex relaxation and distribution matching. A rapid 3D segmentation of the RV in cardiac MRI via distribution matching constraints on segment shape and appearance is also investigated. These algorithms only require a single subject for training and a very simple user input, which amounts to one click. The solution is sought following the optimization of functionals containing probability product kernel constraints on the distributions of intensity and geometric features. The formulations lead to challenging optimization problems, which are not directly amenable to convex-optimization techniques. For each functional, the problem is split into a sequence of sub-problems, each of which can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Finally, an information-theoretic based artificial neural network (ANN) is proposed for normal/abnormal LV myocardium motion classification. Using the LV segmentation results, the LV cavity points is estimated via a Kalman filter and a recursive dynamic Bayesian filter. However, due to the similarities between the statistical information of normal and abnormal points, differentiating between distributions of abnormal and normal points is a challenging problem. The problem was investigated with a global measure based on the Shannon\u27s differential entropy (SDE) and further examined with two other information-theoretic criteria, one based on Renyi entropy and the other on Fisher information. Unlike the existing information-theoretic studies, the approach addresses explicitly the overlap between the distributions of normal and abnormal cases, thereby yielding a competitive performance. I further propose an algorithm based on a supervised 3-layer ANN to differentiate between the distributions farther. The ANN is trained and tested by five different information measures of radial distance and velocity for points on endocardial boundary

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

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

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 left ventricle tracking using localized anatomical affine optical flow

Fast left ventricle tracking using localized anatomical affine optical flowIn daily clinical cardiology practice, left ventricle (LV) global and regional function assessment is crucial for disease diagnosis, therapy selection, and patient follow-up. Currently, this is still a time-consuming task, spending valuable human resources. In this work, a novel fast methodology for automatic LV tracking is proposed based on localized anatomically constrained affine optical flow. This novel method can be combined to previously proposed segmentation frameworks or manually delineated surfaces at an initial frame to obtain fully delineated datasets and, thus, assess both global and regional myocardial function. Its feasibility and accuracy were investigated in 3 distinct public databases, namely in realistically simulated 3D ultrasound, clinical 3D echocardiography, and clinical cine cardiac magnetic resonance images. The method showed accurate tracking results in all databases, proving its applicability and accuracy for myocardial function assessment. Moreover, when combined to previous state-of-the-art segmentation frameworks, it outperformed previous tracking strategies in both 3D ultrasound and cardiac magnetic resonance data, automatically computing relevant cardiac indices with smaller biases and narrower limits of agreement compared to reference indices. Simultaneously, the proposed localized tracking method showed to be suitable for online processing, even for 3D motion assessment. Importantly, although here evaluated for LV tracking only, this novel methodology is applicable for tracking of other target structures with minimal adaptations.The authors acknowledge funding support from FCT - Fundacao para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/93443/2013 (S. Queiros) and SFRH/BD/95438/2013 (P. Morais), and by the project ’PersonalizedNOS (01-0145-FEDER-000013)’ co-funded by Programa Operacional Regional do Norte (Norte2020) through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Algorithmic assessment of cardiac viability using magnetic resonance imaging

MRI is a non-invasive imaging method which produces high resolution images of human tissues from inside the human body. Due to its outstanding ability, it is quickly becoming a major tool for medical and clinical studies, including high profile areas such as neurology, oncology, cardiology and etc. MRI technology developed relatively slowly compared to other methods such as x-ray. A decade ago, it took more than 5 minutes to construct an MR image. However more recently, with several significant inventions such as echo planar imaging and steady state free procession techniques, the acquisition time of MRI has significantly reduced. At present, it is possible to capture dozens of MR images in a second. Those techniques are generally called ultra-fast MRI. The fast MR acquisition techniques enable us to extend our studies to the moving tissues such as the myocardium. Using the ultra-fast MRI, multiple images can be acquired during a cardiac cycle allowing the construction of cardiac cinematographic MR images. Cardiac motion can therefore be revealed. Abnormal cardiac motion is often related to cardiac diseases such as ischaemic myocardium and myocardial infarction. With advanced MRI techniques, cardiac diseases can be more specifically defined. For example, the late contrast enhanced MRI highlights acute myocardial infarction. The first-pass perfusion MRI suggests the existence of ischaemic myocardium. At the present time the majority of the analysis of MR images can be performed either qualitatively or quantitatively. The qualitative assessment is an eye-ball assessment of the images on a MRI workstation, which is subjective and inaccurate. The quantitative assessment of MR image relies on the computer technologies of both hardware and software. In recent years, the demands for the quantitative assessment of MR images have increased sharply. Many so-called computer aided diagnosis systems were developed to process data either more accurately or more efficiently. In this study, we developed an algorithmic method to analyse the late contrast enhanced MR images, revealing the so-called hibernating myocardium. The algorithm is based on an efficient and robust image registration algorithm. Using the image registration algorithm, we are able to integrate the static late contrast enhanced MR image with its corresponding cardiac cinematography MR images, and so constructing cardiac CINE late enhanced MR images. Our algorithm was tested on 20 subjects. In each of the subject, the mean left ventricle diastolic volume and systolic volume was measured by planimetry from both the original CINE images and the constructed late enhanced CINE images. The results are: left ventricle diastolic volume (original / constructed) = 206 / 215 ml, p = 0.35. Left ventricle systolic volume (original / constructed) = 129 / 123 ml, p = 0.33. With our algorithm, the cardiac motion and the myocardial infarction can therefore be studied simultaneously to locate the hibernating myocardium which moves abnormally. The accurate location of the hibernating myocardium is important because it could turn into the irreversible myocardial infarction. On the other hand, with proper medical treatment or cardiac surgery, the hibernating myocardium could be revitalised. The experimental results show there are no significant differences between the artificial cine late contrast enhanced MR images and the original cinematography MR images in left ventricle diastolic volume, left ventricle systolic volume. The method therefore appears promising as an improved cardiac viability assessment tool. In addition, we extended the method to a semi-automatic cardiac contour definition algorithm, which has produced a satisfactory result in contour definition for cardiac cinematography MR images from 34 subjects including 20 healthy volunteers and 14 patients. Although it is a semi-automatic method, the diagnosis time could be significantly reduced compared to the manual method. The algorithm was preliminarily tested on 10 first-pass perfusion MR sequences and 10 aortic MR sequences. The experimental results were satisfactory. Although, minor manual correction is required on some occasions, we believe our method could be clinically useful for the study of cardiac cinematography MR images, first-pass perfusion MR images and aortic MR images

Computer Aided Analysis of Late Gadolinium Enhanced Cardiac MRI

Ph.DDOCTOR OF PHILOSOPH

Medical Image Segmentation Combining Level Set Method and Deep Belief Networks

Medical image segmentation is an important step in medical image analysis, where the main goal is the precise delineation of organs and tumours from medical images. For instance there is evidence in the field that shows a positive correlation between the precision of these segmentations and the accuracy observed in classification systems that use these segmentations as their inputs. Over the last decades, a vast number of medical image segmentation models have been introduced, where these models can be divided into five main groups: 1) image-based approaches, 2) active contour methods, 3) machine learning techniques, 4) atlas-guided segmentation and registration and 5) hybrid models. Image-based approaches use only intensity value or texture for segmenting (i.e., thresholding technique) and they usually do not produce precise segmentation. Active contour methods can use an explicit representation (i.e., snakes) with the goal of minimizing an energy function that forces the contour to move towards strong edges and maintains the contour smoothness. The use of implicit representation in active contour methods (i.e., level set method) embeds the contour as zero level set of a higher dimensional surface (i.e., the curve representing the contour does not need to be parameterized as in the Snakes model). Although successful, the main issue with active contour methods is the fact that the energy function must contain terms describing all possible shape and appearance variations, which is a complicated task given that it is hard to design by hand all these terms. Also, this type of active contour methods may get stuck at image regions that do not belong to the object of interest. Machine learning techniques address this issue by automatically learning shape and appearance models using annotated training images. Nevertheless, in order to meet the high accuracy requirements of medical image analysis applications, machine learning methods usually need large and rich training sets and also face the complexity of the inference process. Atlas-guided segmentation and registration use an atlas image, which is constructed based on manually segmentation images. The new image is segmented by registering it with the atlas image. These techniques have been applied successfully in many applications, but they still face some issues, such as their ability to represent the variability of anatomical structure and scale in medical image, and the complexity of the registration algorithms. In this work, we propose a new hybrid segmentation approach by combining a level set method with a machine learning approach (deep belief network). Our main objective with this approach is to achieve segmentation accuracy results that are either comparable or better than the ones produced with machine learning methods, but using relatively smaller training sets. These weaker requirements on the size of training sets is compensated by the hand designed segmentation terms present in typical level set methods, that are used as prior information on the anatomy to be segmented (e.g., smooth contours, strong edges, etc.). In addition, we choose a machine learning methodology that typically requires smaller annotated training sets, compared to other methods proposed in this field. Specifically, we use deep belief networks, with training sets consisting to a large extent of un-annotated training images. In general, our hybrid segmentation approach uses the result produced by the deep belief network as a prior in the level set evolution. We validate this method on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 left ventricle segmentation challenge database and on the Japanese Society of Radiological Technology (JSRT) lung segmentation dataset. The experiments show that our approach produces competitive results in the field in terms of segmentation accuracy. More specifically, we show that the use of our proposed methodology in a semi-automated segmentation system (i.e., using a manual initialization) produces the best result in the field in both databases above, and in the case of a fully automated system, our method shows results competitive with the current state of the art.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201