In 2017, pneumonia was the primary diagnosis for 1.3 million visits to the Emergency Department (ED) in the United States. The mortality rate was estimated to be 5%-10% of hospitalized patients, whereas it rises to 30% for severe cases admitted to the Intensive Care Unit (ICU). Among all cases admitted to ED, 30% were misdiagnosed, and they did not suffer from pneumonia, which raises a flag for the need for more accurate diagnosis methods. Several methods for pneumonia detection were recently developed using AI in general and more specifically, using deep neural networks. Even though it worth

In this work, we propose an algorithm for segmenting the ascending and descending aorta from magnetic resonance phase contrast images, also referred to as MR flow imaging. The proposed algorithm is based on the active contour model combined with some refinements. In addition, false segmentation results due to severe image artifacts are automatically detected and corrected. The developed algorithm features three practical advantages: (1) fast; (2) requires minimal user interaction; and (3) robust to the changes in the algorithm parameters (e.g. same parameter set is used for all datasets). The

Imaging of the heart anatomy and function using magnetic resonance imaging (MRI) is an important diagnosis tool for heart diseases. Several techniques have been developed to increase the contrast-to-noise ratio (CNR) between myocardium and background. Recently, a technique that acquires cine cardiac images with black-blood contrast has been proposed. Although the technique produces cine sequence of high contrast, it suffers from elevated noise which limits the CNR. In this paper, we study the performance and efficiency of applying a Bayes classifier to remove background noise. Real MRI data is

Breast cancer is the main cause of death for women between the ages of 35 to 55. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. Microcalcifications are among the earliest signs of a breast carcinoma. Actually, as radiologists point out, microcalcifications can be the only mammographic sign of non-palpable breast disease which are often overseen in the mammogram. In this paper a method is proposed to develop a Computer-Aided Diagnostic system for classification of microcalcifications in digital mammograms, it splits into three-step process

We propose a model that describes the interaction of several Brain Regions based on Functional Magnetic Resonance Imaging (FMRI) time series to make inferences about functional integration and segregation within the human brain. The method is demonstrated using dynamic causal modeling (OeM) using real data to show how such models are able to characterize interregional dependence. We extend estimating and reviewing designed model to characterize the interactions between regions. A further benefit is to estimate the effective connectivity between these regions. All designs, estimates, reviews

Different heart tissue identification is important for therapeutic decision-making in patients with myocardial infarction (MI), this provides physicians with a better clinical decision-making tool. Composite Strain Encoding (C-SENC) is an MRI acquisition technique that is used to acquire cardiac tissue viability and contractility images. It combines the use of blackblood delayed-enhancement (DE) imaging to identify the infracted (dead) tissue inside the heart muscle and the ability to image myocardial deformation from the strain-encoding (SENC) imaging technique. In this work, various machine

Optical flow (OF) tracking of the myocardium contours has a potential in segmenting the myocardium in time sequences of cardiac medical images. Nevertheless, to estimate the displacement field of the contour points, a number of assumptions are required to solve an under-determined set of optical flow equations. In this work, a new framework is proposed to solve the OF tracking problem using greedy optimisation algorithm. The new framework allows different types of constraints such as motion invariance, shape and topology to be applied in a unified way. The developed methods are applied to a

Automatic localization of the left ventricle (LV) in cardiac MRI images is an essential step for automatic segmentation, functional analysis, and content based retrieval of cardiac images. In this paper, we introduce a new approach based on deep Convolutional Neural Network (CNN) to localize the LV in cardiac MRI in short axis views. A six-layer CNN with different kernel sizes was employed for feature extraction, followed by Softmax fully connected layer for classification. The pyramids of scales analysis was introduced in order to take account of the different sizes of the heart. A publically

Active shape models (ASM) showed to have potential for segmenting the right ventricle (RV) in cardiac magnetic resonance images (MRIs). Nevertheless, the large variability and complexity of the RV shape do not allow for concisely capturing all possible shape variations among patients and anatomical cross-sections. Noticeably, the latter increases the number of iterations required to converge to a proper solution and reduces the segmentation accuracy. In this study, the authors propose a new ASM framework that can model the RV shape in short-axis cardiac MRI images. In this framework, the RV

Alternative splicing is a vital process for regulating gene expression and promoting proteomic diversity. It plays a key role in tissue-specific expressed genes. This specificity is mainly regulated by splicing factors that bind to specific sequences called splicing regulatory elements (SREs). Here, we report a genome-wide analysis to study alternative splicing on multiple tissues, including brain, heart, liver, and muscle. We propose a pipeline to identify differential exons across tissues and hence tissue-specific SREs. In our pipeline, we utilize the DEXSeq package along with our previously