Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression

Background: Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment. Purpose: To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans. Study Type: Cross-sectional survey; diagnostic accuracy. Subjects: In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database. Field Strength/Sequence: 1.5T, steady-state free precession. Assessment: Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume. Statistical Tests: Paired t-tests compared to previous work. Results: Tested against the LVSC database, we obtained 0.7760.11 (Jaccard index) and 1.3360.71mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was 17.2613.0mL and –19.8618.8mL for the endsystolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase. Data Conclusion: A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility. Level of Evidence: 3 Technical Efficacy: Stage 2Li Kuo Tan, Robert A. McLaughlin, Einly Lim, Yang Faridah Abdul Aziz, and Yih Miin Lie

In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography

The use of Micro-Computed Tomography (MicroCT) for in vivo studies of small animals as models of human disease has risen tremendously due to the fact that MicroCT provides quantitative high-resolution three-dimensional (3D) anatomical data non-destructively and longitudinally. Most importantly, with the development of a novel preclinical iodinated contrast agent called eXIA160, functional and metabolic assessment of the heart became possible. However, prior to the advent of commercial MicroCT scanners equipped with X-ray flat-panel detector technology and easy-to-use cardio-respiratory gating, preclinical studies of cardiovascular disease (CVD) in small animals required a MicroCT technologist with advanced skills, and thus were impractical for widespread implementation. The goal of this work is to provide a practical guide to the use of the high-speed Quantum FX MicroCT system for comprehensive determination of myocardial global and regional function along with assessment of myocardial perfusion, metabolism and viability in healthy mice and in a cardiac ischemia mouse model induced by permanent occlusion of the left anterior descending coronary artery (LAD)

Analysis of myocardial contractility with magnetic resonance

Heart failure has considerable morbidity and poor prognosis. An understanding of the underlying mechanics governing myocardial contraction is a prerequisite for interpreting and predicting changes induced by heart disease. Gross changes in contractile behaviour of the myocardium are readily detected with existing techniques. For more subtle changes during early stages of cardiac dysfunction, however, it requires a sensitive method for measuring, as well as a precise criterion for quantifying, normal and impaired myocardial function. Cardiovascular Magnetic Resonance (CMR) imaging is emerging as an important clinical tool because of its safety, versatility, and the high quality images it produces that allow accurate and reproducible quantification of cardiac structure and function. Traditional CMR approaches for measuring contractility rely on tagging of the myocardium with fiducial markers and require a lengthy and often subjective dependant post-processing procedure. The aim of this research is to develop a new technique, which uses velocity as a marker for the visualisation and assessment of myocardial contractility. Two parallel approaches have been investigated for the assessment of myocardial velocity. The first of these is haimonic phase (HARP) imaging. HARP imaging allows direct derivation of myocardial velocity and strain without the need of further user interaction. We investigated the effect of respiration on the accuracy of the derived contractility, and assessed the clinical applicability and potential pitfalls of the technique by analysing results from a group of patients with hypertrophic cardiomyopathy. The second technique we have investigated is the direct measurement of myocardial velocity with phase contrast myocardial velocity mapping. The imaging sequence used employs effective blood saturation for reducing flow induced phase errors within the myocardium. View sharing was used to improve the temporal resolution, which permitted acquisition of 3D velocity information throughout the cardiac cycle in a single breath-hold, enabling a comprehensive assessment of strain rate of the left ventricle. One key factor that affects the derivation of myocardial contractility based on myocardial velocity is the practical inconsistency of the velocity data. A novel iterative optimisation scheme by incorporating the incompressibility constraint was developed for the restoration of myocardial velocity data. The method allowed accurate assessment of both in-plane and through-plan strain rates, as demonstrated with both synthetic and in vivo data acquired from normal subjects and ischaemic patients. To further enhance the clinical potential of the technique and facilitate the visual assessment of contractile abnormality with myocardial velocity mapping, a complementary analysis framework, named Virtual Tagging, has been developed. The method used velocity data in all directions combined with a finite element mesh incorporating geometrical and physical constraints. The Virtual Tagging framewoik allowed velocity measurements to be used for calculating strain distribution within the 3D volume. It also permitted easy visualisation of the displacement of the tissue, akin to traditional CMR tagging. Detailed validation of the technique is provided, which involves both numerical simulation and in vitro phantom experiments. The main contribution of this thesis is in the improvement of the effectiveness and quality of quantitative myocardial contractility analysis from both sequence design and medical image computing perspectives. It is aimed at providing a sensitive means of detecting subtle as well as gross changes in contractile behaviour of the myocardium. The study is expected to provide a clinically viable platform for functional correlation with other functional measures such as myocardial perfusion and diffusion, and to serve as an aid for further understanding of the links between intrinsicOpen acces

Comparison of T1-maps and late gadolinium enhancement images in the detection of Myocardial Fibrosis in Hypertrophic Cardiomyopathy

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica, 2021, Universidade de Lisboa, Faculdade de CiênciasHypertrophic Cardiomyopathy (HCM) is characterized as an abnormal and heterogeneous thickening of the Left Ventricle (LV) wall. HCM is the leading cause of sudden cardiac death in children and young people, with an estimated prevalence of 1:500 in the general population. Myocardial fibrosis is the key histopathological hallmark in HCM and is presented in different patterns: interstitial diffuse fibrosis which, if not treated, evolves to replacement fibrosis. Cardiac Magnetic Resonance (CMR) imaging has been used for the detection and quantification of myocardial fibrosis. The Late Gadolinium Enhancement (LGE) technique is the primary tool for non-invasive tissue characterization, particularly for replacement fibrosis. Conversely, T1 mapping is commonly used for the detection of diffuse interstitial fibrosis, frequently missed using LGE. The clear disadvantage of LGE relies on the need to inject contrast agents that, despite being considered safe, may accumulate in the body for years and potentially cause nephrogenic systemic fibrosis in end-stage chronic kidney disease patients. The capability of native T1 mapping identifying not only diffuse interstitial but also replacement fibrosis would play a pivotal role in HCM diagnosis. The potential of native T1 mapping for a cheaper and non-contrast HCM assessment needs to be further studied. A database of 15 HCM patients, without and with fibrosis, was acquired at Hospital da Luz, Lisboa. In this project, (1) an extensive image preprocessing pipeline was applied to aim for the best possible spatial alignment of the myocardium between the two modalities (native T1 mapping and LGE); (2) the mean native T1 values of individuals without and with the presence of scarred tissue were examined; (3) a pixel-by-pixel analysis was performed to investigate if there is a correlation between fibrotic tissue in LGE and hyperintense regions in native T1 mapping; (4) a Texture Analysis (TA) was performed to study if texture information of native T1 mapping could provide differential diagnosis or prognostic information beyond mean T1 values. The first step was the most longstanding and challenging process. The registration of T1 and LGE images is difficult due to the different intensity profiles. The registration of the myocardial masks using a model with rigid, affine, and free-form deformation transformations revealed to be the best methodology. Mean native T1 values were not increased in patients with scarred tissue. Regarding the third aim, no clear intensity correlation between techniques was observed, which suggests the need for the TA. Seven features (in a total of 350) were selected to distinguish between cardiac segments without and with fibrotic tissue using a ML (Machine Learning) algorithm that finds the features that most contribute to distinguish the two groups. Four first-order features distinguish the cohorts due to the presence of scarred tissue - hyperintense zones - and three texture features suggest that the fibrotic remodeling in the myocardium of HCM patients might be associated with a more heterogeneous tissue texture. A Receiver Operating Characteristics (ROC) analysis was performed and revealed that the Cluster Prominence is the feature that best distinguishes sections without and with fibrotic tissue (accuracy of 70%) but with low sensitivity (65%) and low specifity (64%). A model with the 90th Percentile feature revealed an accuracy of 64%, sensitivity of 71% and specificity of 57%. Studying the Variance feature, the achieved accuracy was 63%, with 66% of sensitivity and 60% of specificity. The remaining features yielded lower accuracy values than the ones previously mentioned, but all of them higher than 50%. The low sensitivity and specificity of the best three models suggest that analysing these values considering these features may help cardiologists to identify focal fibrosis regions and avoid contrast injection methods but may not provide an accurate diagnosis of the presence of fibrotic tissue alone. Further research on the correlation of native T1 mapping and LGE cardiac images is highly recommended to develop a contrast-agent-free technology to replace LGE.A Cardiomiopatia Hipertrófica (do inglês, HCM) é descrita por um espessamento anormal e heterogéneo da parede do ventrículo esquerdo (do inglês, LV). A HCM é a principal causa de morte súbita cardíaca em crianças e jovens, com uma prevalência estimada de 1:500 na população em geral. Esta doença é, na sua maioria, hereditária, e causada por variantes nos genes da proteína do sarcómero (predominantemente MYH7 e MYBPC3). A fibrose do miocárdio é a principal marca histopatológica da HCM e apresenta-se em diferentes padrões: fibrose intersticial difusa que, se não tratada, evolui para fibrose focal. A fibrose é caracterizada por um aumento da deposição de colagénio, que afeta a viabilidade do miocárdio. A imagem de Ressonância Magnética Cardíaca (do inglês, CMR) tem sido usada para a deteção e quantificação de fibrose do miocárdio. A técnica de Realce Tardio (do inglês, LGE) é a principal ferramenta para caracterização não invasiva de tecidos, particularmente de fibrose focal. Em contrapartida, o mapeamento T1 é a técnica mais utilizada para deteção de fibrose intersticial difusa, frequentemente não detetada usando LGE. A clara desvantagem do LGE reside na necessidade de injeção de agentes de contraste. Apesar destes agentes serem considerados seguros, frequentemente causam alergias, podem-se acumular no corpo, por anos, e podem causar fibrose sistémica nefrogénica em pacientes com doença renal crónica terminal. A capacidade do mapeamento T1 nativo identificar, não só a fibrose intersticial difusa mas também a fibrose focal, desempenharia um papel fundamental no diagnóstico da HCM. Consequentemente, é de extrema importância estudar o potencial do mapeamento T1 nativo para uma avaliação desta patologia sem contraste e, desta forma, eliminar os riscos associados à injeção de contraste e reduzir os custos e tempo de preparação associados à utilização de gadolínio. Uma base de dados de 15 pacientes com HCM, com e sem fibrose, previamente adquirida no Hospital da Luz, Lisboa, foi analisada. Neste projeto, (1) aplicou-se um extenso conjunto de passos de pré-processamento de imagem para alcançar a melhor técnica possível de alinhamento espacial do miocárdio entre as duas modalidades (mapeamento T1 nativo e Realce Tardio); (2) após a divisão do miocárdio em 6 secções, como sugerido pela American Heart Association, examinaram-se os valores médios de T1, para cada secção, de indivíduos sem e com presença de tecido cicatricial; (3) realizou-se uma análise pixel a pixel para investigar se existe uma correlação entre o tecido fibrótico em LGE e as regiões hiperintensas no mapeamento T1 nativo; (4) realizou-se uma análise de textura para estudar se a informação de textura do mapeamento T1 nativo poderia fornecer um diagnóstico diferencial ou informação prognóstica além dos valores médios de T1 nativo. A primeira etapa revelou ser o processo mais demorado e desafiante. O batimento cardíaco e o ciclo respiratório representam dois desafios no registo de imagens cardíacas. Para além dos comuns desafios em alinhamento de imagens cardíacas da mesma modalidade, alinhar imagens de diferentes modalidades torna-se um processo mais complexo. Em primeiro lugar, o registo de imagens T1 e de LGE é dificultado pelos distintos perfis de intensidade das duas modalidades. Em segundo lugar, a aquisição de imagens de Realce Tardio ocorre cerca de 7 minutos após a aquisição do mapeamento T1, e o movimento dos pacientes durante este intervalo de tempo é uma fonte adicional de erro. Diferentes softwares foram utilizados, e uma imagem sintética ponderada em T1 foi criada, com o intuito de apresentar intensidades mais similares à imagem a ser alinhada (imagem de LGE). O registo das máscaras miocárdicas por meio de um modelo com transformações rígida, afim e deformações livres mostrou ser a melhor metodologia a aplicar. Os valores médios de T1 nativo não aumentaram significativamente em pacientes com tecido cicatricial, apesar de haver um aumento dos valores de T1 nativo em determinadas secções, em cortes basais e intermédios. Relativamente ao terceiro objetivo abordado, não foi observada uma clara correlação de intensidades entre as técnicas, o que reforçou a necessidade de uma análise de textura (do inglês, TA). Esta análise revelou as sete melhores características (num total de 350) que distinguem segmentos cardíacos sem e com tecido fibrótico, aplicando um método de Machine Learning (do inglês, ML) que identificou, sequencialmente, as features que adicionavam mais informação ao modelo que distinguia os dois grupos de segmentos. Quatro características de primeira ordem distinguem os segmentos devido à presença de tecido cicatricial - zonas hiperintensas - e três características de textura sugerem que a remodelação fibrótica no miocárdio de pacientes com HCM pode estar associada a uma textura mais heterogénea. Foi implementada uma análise ao desempenho de modelos com as features selecionadas, que revelou que a Cluster Prominence é a característica que melhor distingue secções sem e com tecido fibrótico, apesar de com baixa sensibilidade (65%) e baixa especificidade (64%). Um modelo que analisa o Percentil 90 revelou uma precisão de 64%, sensibilidade de 71% e especificidade de 57%. No estudo da Variância, a precisão foi de 63%, a sensibilidade 66% e a especificidade 60%. As restantes features apresentaram valores de precisão inferiores aos mencionados mas acima de 50%. Um modelo com a combinação das sete features selecionadas não melhorou a performance do modelo (precisão de 62%, sensibilidade de 75% e 49% de especificidade). A baixa sensibilidade e especificidade sugerem que a análise desses valores nessas características pode ajudar os cardiologistas a identificar regiões focais de fibrose e evitar métodos de injeção de contraste, mas pode não fornecer um diagnóstico preciso da presença de tecido fibrótico por si só. Em futuras aquisições, encontrar valores semelhantes nas features acima mencionadas, principalmente na Cluster Prominence, em novos dados, poderia ajudar os cardiologistas a identificar regiões de fibrose focal. Desta forma, não seria necessário analisar imagens de Realce Tardio, o que se traduziria na eliminação de injeção de agentes de contraste. Pesquisas adicionais focadas na correlação do mapeamento T1 nativo e imagens cardíacas de LGE são de extrema importância para desenvolver uma tecnologia independente da injeção de agentes de contraste, que substitua o Realce Tardio

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

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

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%

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