Taking into account the complexity and limitations of clinical assessment in hypertrophic cardiomyopathy (HCM), imaging techniques play an essential role in the evaluation of patients with this disease. Thus, in HCM patients, imaging provides solutions for most clinical needs, from diagnosis to prognosis and risk stratification, from anatomical and functional assessment to ischaemia detection, from metabolic evaluation to monitoring of treatment modalities, from staging and clinical profiles to follow-up, and from family screening and preclinical diagnosis to differential diagnosis. Accordingly, a multimodality imaging (MMI) approach (including echocardiography, cardiac magnetic resonance, cardiac computed tomography, and cardiac nuclear imaging) is encouraged in the assessment of these patients. The choice of which technique to use should be based on a broad perspective and expert knowledge of what each technique has to offer, including its specific advantages and disadvantages. Experts in different imaging techniques should collaborate and the different methods should be seen as complementary, not as competitors. Each test must be selected in an integrated and rational way in order to provide clear answers to specific clinical questions and problems, trying to avoid redundant and duplicated information, taking into account its availability, benefits, risks, and cost.

The term 'athlete's heart' refers to a clinical picture characterized by a slow heart rate and enlargement of the heart. A multi-modality imaging approach to the athlete's heart aims to differentiate physiological changes due to intensive training in the athlete's heart from serious cardiac diseases with similar morphological features. Imaging assessment of the athlete's heart should begin with a thorough echocardiographic examination.Left ventricular (LV) wall thickness by echocardiography can contribute to the distinction between athlete's LV hypertrophy and hypertrophic cardiomyopathy (HCM). LV end-diastolic diameter becomes larger (>55 mm) than the normal limits only in end-stage HCM patients when the LV ejection fraction is <50%. Patients with HCM also show early impairment of LV diastolic function, whereas athletes have normal diastolic function.When echocardiography cannot provide a clear differential diagnosis, cardiac magnetic resonance (CMR) imaging should be performed.With CMR, accurate morphological and functional assessment can be made. Tissue characterization by late gadolinium enhancement may show a distinctive, non-ischaemic pattern in HCM and a variety of other myocardial conditions such as idiopathic dilated cardiomyopathy or myocarditis. The work-up of athletes with suspected coronary artery disease should start with an exercise ECG. In athletes with inconclusive exercise ECG results, exercise stress echocardiography should be considered. Nuclear cardiology techniques, coronary cardiac tomography (CCT) and/or CMR may be performed in selected cases. Owing to radiation exposure and the young age of most athletes, the use of CCT and nuclear cardiology techniques should be restricted to athletes with unclear stress echocardiography or CMR.

Abstract-We present a new supervised learning model designed for the automatic segmentation of the left ventricle (LV) of the heart in ultrasound images. We address the following problems inherent to supervised learning models: 1) the need of a large set of training images; 2) robustness to imaging conditions not present in the training data; and 3) complex search process. The innovations of our approach reside in a formulation that decouples the rigid and nonrigid detections, deep learning methods that model the appearance of the LV, and efficient derivative-based search algorithms. The functionality of our approach is evaluated using a data set of diseased cases containing 400 annotated images (from 12 sequences) and another data set of normal cases comprising 80 annotated images (from two sequences), where both sets present long axis views of the LV. Using several error measures to compute the degree of similarity between the manual and automatic segmentations, we show that our method not only has high sensitivity and specificity but also presents variations with respect to a gold standard (computed from the manual annotations of two experts) within interuser variability on a subset of the diseased cases. We also compare the segmentations produced by our approach and by two state-of-the-art LV segmentation models on the data set of normal cases, and the results show that our approach produces segmentations that are comparable to these two approaches using only 20 training images and increasing the training set to 400 images causes our approach to be generally more accurate. Finally, we show that efficient search methods reduce up to tenfold the complexity of the method while still producing competitive segmentations. In the future, we plan to include a dynamical model to improve the performance of the algorithm, to use semisupervised learning methods to reduce even more the dependence on rich and large training sets, and to design a shape model less dependent on the training set.

The automatic segmentation of the left ventricle of the heart in ultrasound images has been a core research topic in medical image analysis. Most of the solutions are based on low-level segmentation methods, which uses a prior model of the appearance of the left ventricle, but imaging conditions violating the assumptions present in the prior can damage their performance. Recently, pattern recognition methods have become more robust to imaging conditions by automatically building an appearance model from training images, but they present a few challenges, such as: the need of a large set of training images, robustness to imaging conditions not present in the training data, and complex search process. In this paper we handle the second problem using the recently proposed deep neural network and the third problem with efficient searching algorithms. Quantitative comparisons show that the accuracy of our approach is higher than state-of-the-art methods. The results also show that efficient search strategies reduce ten times the run-time complexity.Index Terms-Segmentation of the left ventricle of the heart, deep neural networks, optimization algorithms

Wireless communications are being used for enhancing transport systems .In order to achieve these goals, some communication technologies are specially designed to support Vehicleto-everything (V2X) functionalities. C-V2X (Cellular Vehicle-toeverything) or LTE-V2X and ITS-G5 are one of the existing solutions respectively standardized by 3GPP (3rd Generation Partnership Project) and ETSI (European Telecommunications Standards Institute). While ITS-G5 is a dedicated wireless network for C-ITS applications, LTE-V2X shares the network resources with other LTE applications used on mobile devices. In this paper, we give an insight on both technologies by briefly describing their communication mechanisms. Then, we compare their performances under different use cases using a networking and vehicular simulation platform. Results show that ITS-G5 outperforms LTE-V2X (mode 3) in case of the presence of concurrent LTE data traffic with V2X service. Throughout the conducted scenarios, we demonstrate the negative impact of handover on LTE-V2X eNB scheduled mode.