Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section

Automated image analysis techniques for cardiovascular magnetic resonance imaging

The introductory chapter provides an overview of various aspects related to quantitative analysis of cardiovascular MR (CMR) imaging studies. Subsequently, the thesis describes several automated methods for quantitative assessment of left ventricular function from CMR imaging studies. Several novel computer algorithms are introduced and validated for automated segmentation of short-axis CMR images and validated by comparing functional results derived from automated segmentation with results derived from manually traced contours. In addition an automated method is presented for assessment of flow through the aorta based on Phase-Contrast flow velocity mapping MRI. Finally a method is presented for accurate assessment of the thickness of the left ventricular myocardium taking advantage of the three-dimensional nature of MRI.UBL - phd migration 201

BIVENTRICULAR FINITE ELEMENT MODELING AND QUANTIFICATION OF 3D LANGRAGIAN STRAINS AND TORSION USING DENSE MRI

Statistical data suggests that increased use of evidence-based medical therapies has largely contributed to the decrease in American death rate caused by heart disease. And my studies are about two applications of magnetic resonance imaging (MRI) as a non-invasive approach in evidence-based health care research. In my first study, the achievement of a pulmonary valve replacement surgery was assessed on a patient with tetralogy of Fallot (TOF). In order to evaluate the remodeling of right ventricle, two biventricular finite element models were built up for pre-surgical images and post-surgical images. In my second study, 3D Lagrangian strains and torsion in the left ventricle of ten rats were investigated using Displacement ENcoding with Stimulated Echoes (DENSE) cardiac magnetic resonance (CMR) images. Tools written in MATLAB were developed for 2D contouring, 3D modeling, strain and torsion computations, and statistical comparison across subjects

Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images

Left ventricular (LV) volumes estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address direct LV volumes prediction task. Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN). We study the end-to-end LV volumes prediction method in items of the data preprocessing, networks structure, and multi-views fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR). Second, we propose a new networks structure for end-to-end LV volumes estimation. Third, we explore the representational capacity of different slices, and propose a fusion strategy to improve the prediction accuracy. Results: The evaluation results show that the proposed method outperforms other state-of-the-art LV volumes estimation methods on the open accessible benchmark datasets. The clinical indexes derived from the predicted volumes agree well with the ground truth (EDV: R2=0.974, RMSE=9.6ml; ESV: R2=0.976, RMSE=7.1ml; EF: R2=0.828, RMSE =4.71%). Conclusion: Experimental results prove that the proposed method may be useful for LV volumes prediction task. Significance: The proposed method not only has application potential for cardiac diseases screening for large-scale CMR data, but also can be extended to other medical image research fieldsComment: to appear on Transactions on Biomedical Engineerin

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

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

Automatic Assessment of Cardiac Left Ventricular Function Via Magnetic Resonance Images

Automating global and segmental (regional) assessments of cardiac Left Ventricle (LV) function in Magnetic Resonance Images (MRI) has recently sparked an impressive research effort, which has resulted a number of techniques delivering promising performances. However, despite such an effort, the problem is still acknowledged to be challenging, with substantial room for improvements in regard to accuracy. Furthermore, most of the existing techniques are labour intensive, requiring delineations of the endo- and/or epi-cardial boundaries in all frames of a cardiac sequence. On the one hand, global assessments of LV function focus on estimation of the Ejection Fraction (EF), which quantifies how much blood the heart is pumping within each beat. On the other hand, regional assessments focus on comprehensive analysis of the wall motions within each of the standardized segments of the myocardium, the muscle which contracts and sends the blood out of the LV. In clinical practice, the EF is often estimated via manual segmentations of several images in a cardiac sequence. This is prohibitively time consuming, or via automatic segmentations, which is a challenging and computationally expensive task that may result in high estimation errors. Additionally, the diagnosis of the segmental dysfunction is based on visual LV assessments, which are subject to high inter-observer variability. In this thesis, we propose accurate methods to estimate both global and regional LV function with minimal user inputs in real-time from statistics estimated in MRI. From a simple user input, we build image statistics for all the images in a subject dataset. We demonstrate that these statistics are correlated with regional as well as global LV function. Different machine learning techniques have been employed to find these correlations. The regional dysfunction is investigated in terms of a binary/multi-classification problem. A comprehensive evaluation over 20 subjects demonstrated that the estimated EFs correlated very well with those obtained from independent manual segmentations. Furthermore, comparisons with estimating EF with recent segmentation algorithms show that the proposed method yielded a very competitive performance. For regional binary classification, we report a comprehensive experimental evaluation of the proposed algorithm over 928 cardiac segments obtained from 58 subjects. Compared against ground-truth evaluations by experienced radiologists, the proposed algorithm performed competitively, with an overall classification accuracy of 86.09% and a kappa measure of 0.73. We also report a comprehensive experimental evaluation of the proposed multi-classification algorithm over the same dataset. Compared against ground-truth labels assessed by experienced radiologists, the proposed algorithm yielded an overall 4-class accuracy of 74.14%

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

Análise funcional do ventrículo esquerdo em angio-TC coronária

Doutoramento em Engenharia InformáticaCoronary CT angiography is widely used in clinical practice for the assessment of coronary artery disease. Several studies have shown that the same exam can also be used to assess left ventricle (LV) function. LV function is usually evaluated using just the data from end-systolic and end-diastolic phases even though coronary CT angiography (CTA) provides data concerning multiple cardiac phases, along the cardiac cycle. This unused wealth of data, mostly due to its complexity and the lack of proper tools, has still to be explored in order to assess if further insight is possible regarding regional LV functional analysis. Furthermore, different parameters can be computed to characterize LV function and while some are well known by clinicians others still need to be evaluated concerning their value in clinical scenarios. The work presented in this thesis covers two steps towards extended use of CTA data: LV segmentation and functional analysis. A new semi-automatic segmentation method is presented to obtain LV data for all cardiac phases available in a CTA exam and a 3D editing tool was designed to allow users to fine tune the segmentations. Regarding segmentation evaluation, a methodology is proposed in order to help choose the similarity metrics to be used to compare segmentations. This methodology allows the detection of redundant measures that can be discarded. The evaluation was performed with the help of three experienced radiographers yielding low intraand inter-observer variability. In order to allow exploring the segmented data, several parameters characterizing global and regional LV function are computed for the available cardiac phases. The data thus obtained is shown using a set of visualizations allowing synchronized visual exploration. The main purpose is to provide means for clinicians to explore the data and gather insight over their meaning, as well as their correlation with each other and with diagnosis outcomes. Finally, an interactive method is proposed to help clinicians assess myocardial perfusion by providing automatic assignment of lesions, detected by clinicians, to a myocardial segment. This new approach has obtained positive feedback from clinicians and is not only an improvement over their current assessment method but also an important first step towards systematic validation of automatic myocardial perfusion assessment measures.A angiografia coronária por TC (angio-TC) é prática clínica corrente para a avaliação de doença coronária. Alguns estudos mostram que é também possível utilizar o exame de angio-TC para avaliar a função do ventrículo esquerdo (VE). A função ventricular esquerda (FVE) é normalmente avaliada considerando as fases de fim de sístole e de fim de diástole, apesar de a angio-TC proporcionar dados relativos a diferentes fases distribuídas ao longo do ciclo cardíaco. Estes dados não considerados, devido à sua complexidade e à falta de ferramentas apropriadas para o efeito, têm ainda de ser explorados para que se perceba se possibilitam uma melhor compreensão da FVE. Para além disso, podem ser calculados diferentes parâmetros para caracterizar a FVE e, enquanto alguns são bem conhecidos dos médicos, outros requerem ainda uma avaliação do seu valor clínico. No âmbito de uma utilização alargada dos dados proporcionados pelos angio- TC, este trabalho apresenta contributos ao nível da segmentação do VE e da sua análise funcional. É proposto um método semi-automático para a segmentação do VE de forma a obter dados para as diferentes fases cardíacas presentes no exame de angio- TC. Foi também desenvolvida uma ferramenta de edição 3D que permite aos utilizadores a correcção das segmentações assim obtidas. Para a avaliação do método de segmentação apresentado foi proposta uma metodologia que permite a detecção de medidas de similaridade redundantes, a usar no âmbito da avaliação para comparação entre segmentações, para que tais medidas redundantes possam ser descartadas. A avaliação foi executada com a colaboração de três técnicos de radiologia experientes, tendo-se verificado uma baixa variabilidade intra- e inter-observador. De forma a permitir explorar os dados segmentados, foram calculados vários parâmetros para caracterização global e regional da FVE, para as diversas fases cardíacas disponíveis. Os resultados assim obtidos são apresentados usando um conjunto de visualizações que permitem uma exploração visual sincronizada dos mesmos. O principal objectivo é proporcionar ao médico a exploração dos resultados obtidos para os diferentes parâmetros, de modo a que este tenha uma compreensão acrescida sobre o seu significado clínico, assim como sobre a correlação existente entre diferentes parâmetros e entre estes e o diagnóstico. Finalmente, foi proposto um método interactivo para ajudar os médicos durante a avaliação da perfusão do miocárdio, que atribui automaticamente as lesões detectadas pelo médico ao respectivo segmento do miocárdio. Este novo método obteve uma boa receptividade e constitui não só uma melhoria em relação ao método tradicional mas é também um primeiro passo para a validação sistemática de medidas automáticas da perfusão do miocárdio