2,449 research outputs found
Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model
Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense
Extraction of epi-cardium contours from unseen images using a shape database
Accurate segmentation of the myocardium in cardiac magnetic resonance images can be restricted by image noise and low discrimination between the epi-cardium boundary and other organs. Segmentation of the epi-cardium is important for the calculation of left ventricle mass. In this paper we propose a novel method of epi-cardium segmentation, which firstly segments the left ventricle cavity. The epi-cardium boundary is found using the edge information in the image, and where such information is lacking it enhances the shape with the best fitting scaled segment, taken from a database of expertly assisted hand segmented images. In the final stage the segments are connected using a natural closed spline. The method was evaluated using a leave-one-out strategy on 24 volumes and calculates the coefficient of determination as 0.93 and a root mean square of the point to curve error of 1.54 mm when compared to manually segmented images
Determining candidate polyp morphology from CT colonography using a level-set method
In this paper we propose a level-set segmentation for
polyp candidates in Computer Tomography Colongraphy
(CTC). Correct classification of the candidate
polyps into polyp and non-polyp is, in most cases,
evaluated using shape features. Therefore, accurate
recovery of the polyp candidate surface is important
for correct classification. The method presented in
this paper, evolves a curvature and gradient dependent
boundary to recover the surface of the polyp candidate
in a level-set framework. The curvature term
is computed using a combination of the Mean curvature
and the Gaussian curvature. The results of
the algorithm were run through a classifier for two
complete data-sets and returned 100% sensitivity for
polyps greater than 5mm
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Neural networks are capable of learning rich, nonlinear feature
representations shown to be beneficial in many predictive tasks. In this work,
we use these models to explore the use of geographical features in predicting
colorectal cancer survival curves for patients in the state of Iowa, spanning
the years 1989 to 2012. Specifically, we compare model performance using a
newly defined metric -- area between the curves (ABC) -- to assess (a) whether
survival curves can be reasonably predicted for colorectal cancer patients in
the state of Iowa, (b) whether geographical features improve predictive
performance, and (c) whether a simple binary representation or richer, spectral
clustering-based representation perform better. Our findings suggest that
survival curves can be reasonably estimated on average, with predictive
performance deviating at the five-year survival mark. We also find that
geographical features improve predictive performance, and that the best
performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page
Comparison of 2D and 3D clustering on Short Axis Magnetic Resonance Images of the left ventricle
A comparative study is performed between segmentation results of the left ventricle endo-cardial boundary (inner wall) using a 2D and 3D clustering approach. Segmentation of the endo-cardial boundary is an important process in the evaluation of the left ventricle cavity volume, used to measure heart function. The ejection fraction is an important measurement for the early prognosis and treatment monitoring of many types of Cardiovascular Disease (CVD)
Adaptive evolution of molecular phenotypes
Molecular phenotypes link genomic information with organismic functions,
fitness, and evolution. Quantitative traits are complex phenotypes that depend
on multiple genomic loci. In this paper, we study the adaptive evolution of a
quantitative trait under time-dependent selection, which arises from
environmental changes or through fitness interactions with other co-evolving
phenotypes. We analyze a model of trait evolution under mutations and genetic
drift in a single-peak fitness seascape. The fitness peak performs a
constrained random walk in the trait amplitude, which determines the
time-dependent trait optimum in a given population. We derive analytical
expressions for the distribution of the time-dependent trait divergence between
populations and of the trait diversity within populations. Based on this
solution, we develop a method to infer adaptive evolution of quantitative
traits. Specifically, we show that the ratio of the average trait divergence
and the diversity is a universal function of evolutionary time, which predicts
the stabilizing strength and the driving rate of the fitness seascape. From an
information-theoretic point of view, this function measures the
macro-evolutionary entropy in a population ensemble, which determines the
predictability of the evolutionary process. Our solution also quantifies two
key characteristics of adapting populations: the cumulative fitness flux, which
measures the total amount of adaptation, and the adaptive load, which is the
fitness cost due to a population's lag behind the fitness peak.Comment: Figures are not optimally displayed in Firefo
Identification of body fat tissues in MRI data
In recent years non-invasive medical diagnostic techniques have been used widely in medical investigations. Among the various imaging modalities available, Magnetic Resonance Imaging is very attractive as it produces multi-slice images where the contrast between various types of body tissues such as muscle, ligaments and fat is well defined. The aim of this paper is to describe the implementation of an unsupervised image analysis algorithm able to identify the body fat tissues from a sequence of MR images encoded in DICOM format. The developed algorithm consists of three main steps. The first step pre-processes the MR images in order to reduce the level of noise. The second step extracts the image areas representing fat tissues by using an unsupervised clustering algorithm. Finally, image refinements are applied to reclassify the pixels adjacent to the initial fat estimate and to eliminate outliers. The experimental data indicates that the proposed implementation returns accurate results and furthermore is robust to noise and to greyscale in-homogeneity
Managing the Going Concern Risk in an Uncertain Environment An Analysis of Regulatory Guidance and Financial Relief for the COVID-19 Pandemic
manufacturing company Regal Beloit reports that it has drawn $255 million on its line of credit, even though it has a strong balance sheet and does not currently intend to use the borrowed proceeds, but believes an abundance of caution regarding its cash position is prudent at this time. Management\u27s Responsibility The responsibility to prepare financial statements on a going concern basis under U.S. GAAP and the International Financial Reporting Standards (IFRS) falls on management. Managers must look forward for a reasonable period of time, defined as 12 months from the financial statement issue date or 12 months from the date financials would have been issued for entities that are neither SEC filers nor conduit bond obligors for debt securities that are traded in a public market. Under ASC 205-40, managers must disclose an uncertainty regarding the ability of the business to continue as a going concern if substantial doubt exists when the conditions and events described above, considered in aggregate, indicate that it is probable that the entity will be unable to meet obligations as they become due
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