899 research outputs found
Automated analysis of three-dimensional stress echocardiography
Real-time three-dimensional (3D) ultrasound imaging has been proposed as an alternative for two-dimensional stress echocardiography for assessing myocardial dysfunction and underlying coronary artery disease. Analysis of 3D stress echocardiography is no simple task and requires considerable expertise. In this paper, we propose methods for automated analysis, which may provide a more objective and accurate diagnosis. Expert knowledge is incorporated via statistical modelling of patient data. Methods for identifying anatomical views, detecting endocardial borders, and classification of wall motion are described and shown to provide favourable results. We also present software developed especially for analysis of 3D stress echocardiography in clinical practice. Interobserver agreement in wall motion scoring is better using the dedicated software (96%) than commercially available software not dedicated for this purpose (79%). The developed tools may provide useful quantitative and objective parameters to assist the clinical expert in the diagnosis of left ventricular function
Continuous Ultrasound Speckle Tracking with Gaussian Mixtures
Speckle tracking echocardiography (STE) is now widely used for measuring strain, deformations, and motion in cardiology. STE involves three successive steps: acquisition of individual frames, speckle detection, and image registration using speckles as landmarks. This work proposes to avoid explicit detection and registration by representing dynamic ultrasound images as sparse collections of moving Gaussian elements in the continuous joint space-time space. Individual speckles or local clusters of speckles are approximated by a single multivariate Gaussian kernel with associated linear
trajectory over a short time span. A hierarchical tree-structured model is fitted to sampled input data such that predicted image estimates can be retrieved by regression after reconstruction, allowing a (bias-variance) trade-off between model complexity and image resolution. The inverse image reconstruction problem is solved with an online Bayesian statistical estimation algorithm. Experiments on clinical data could estimate subtle sub-pixel accurate motion that is difficult to capture with frame-to-frame elastic image registration techniques
Automated Analysis of 3D Stress Echocardiography
__Abstract__
The human circulatory system consists of the heart, blood, arteries, veins and
capillaries. The heart is the muscular organ which pumps the blood through the
human body (Fig. 1.1,1.2). Deoxygenated blood flows through the right atrium
into the right ventricle, which pumps the blood into the pulmonary arteries. The
blood is carried to the lungs, where it passes through a capillary network that
enables the release of carbon dioxide and the uptake of oxygen. Oxygenated
blood then returns to the heart via the pulmonary veins and flows from the left
atrium into the left ventricle. The left ventricle then pumps the blood through the
aorta, the major artery which supplies blood to the rest of the body [Drake et a!.,
2005; Guyton and Halt 1996]. Therefore, it is vital that the cardiovascular system
remains healthy. Disease of the cardiovascular system, if untreated, ultimately
leads to the failure of other organs and death
An improved classification approach for echocardiograms embedding temporal information
Cardiovascular disease is an umbrella term for all diseases of the heart. At present, computer-aided echocardiogram diagnosis is becoming increasingly beneficial. For echocardiography, different cardiac views can be acquired depending on the location and angulations of the ultrasound transducer. Hence, the automatic echocardiogram view classification is the first step for echocardiogram diagnosis, especially for computer-aided system and even for automatic diagnosis in the future. In addition, heart views classification makes it possible to label images especially for large-scale echo videos, provide a facility for database management and collection.
This thesis presents a framework for automatic cardiac viewpoints classification of echocardiogram video data. In this research, we aim to overcome the challenges facing this investigation while analyzing, recognizing and classifying echocardiogram videos from 3D (2D spatial and 1D temporal) space. Specifically, we extend 2D KAZE approach into 3D space for feature detection and propose a histogram of acceleration as feature descriptor. Subsequently, feature encoding follows before the application of SVM to classify echo videos.
In addition, comparison with the state of the art methodologies also takes place, including 2D SIFT, 3D SIFT, and optical flow technique to extract temporal information sustained in the video images.
As a result, the performance of 2D KAZE, 2D KAZE with Optical Flow, 3D KAZE, Optical Flow, 2D SIFT and 3D SIFT delivers accuracy rate of 89.4%, 84.3%, 87.9%, 79.4%, 83.8% and 73.8% respectively for the eight view classes of echo videos
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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