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

Application of Mobile Health, Telemedicine and Artificial Intelligence to Echocardiography

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine

Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences. However, currently available techniques lack full automation, limiting reproducibility. We propose a fully automated technique whereby a CMR image sequence is first segmented with a deep, fully convolutional neural network (CNN) architecture, and quadratic basis splines are fitted simultaneously across all cardiac frames using least squares optimization. Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control subjects. In terms of segmentation, we compared state-of-the-art CNN frameworks, U-Net and dilated convolution architectures, with and without temporal context, using cross validation with three folds. Performance relative to expert manual segmentation was similar across all networks: pixel accuracy was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU across foreground classes only was ~85%. Endocardial left ventricular circumferential strain calculated from the proposed pipeline was significantly different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in agreement with the current clinical literature.Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201

Artificial intelligence and automation in valvular heart diseases

Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention

Early detection of cardiac involvement in Miyoshi myopathy: 2D strain echocardiography and late gadolinium enhancement cardiovascular magnetic resonance

<p>Abstract</p> <p>Background</p> <p>Miyoshi myopathy (MM) is an autosomal recessive distal myopathy characterized by early adult onset. Cardiomyopathy is a major clinical manifestation in other muscular dystrophies and an important prognostic factor. Although dysferlin is highly expressed in cardiac muscle, the effect of dysferlin deficiency in cardiac muscle has not been studied. We hypothesized that early myocardial dysfunction could be detected by 2D strain echocardiography and late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR).</p> <p>Method</p> <p>Five consecutive MM patients (3 male) in whom we detected the DYSF gene mutation and age-matched healthy control subjects were included. None of the patients had history of cardiac disease or signs and symptoms of overt heart failure. Patients were studied using 2D strain echocardiography and CMR, with 2D strain being obtained using the Automated Function Imaging technique.</p> <p>Results</p> <p>All patients had preserved left ventricular systolic function. However, segmental Peak Systolic Longitudinal Strain (PSLS) was decreased in 3 patients. Global PSLS was significantly lower in patients with MM than in control subjects (p = 0.005). Basal anterior septum, basal inferior septum, mid anterior, and mid inferior septum PSLS were significantly lower in patients with MM than in control subjects (P < 0.0001, < 0.0001, 0.038 and 0.003, respectively). Four patients showed fibrosis by LGE. The reduced PSLS lesion detected by 2D strain tended to be in the same area as that which showed fibrosis by LGE.</p> <p>Conclusions</p> <p>Patients with MM showed subclinical involvement of the heart. 2D strain and LGE are sensitive methods for detecting myocardial dysfunction prior to the development of cardiovascular symptoms. The prognostic significance of these findings warrants further longitudinal follow-up.</p