240,261 research outputs found
Dynamic Contrast-enhanced MR Imaging of Carotid Atherosclerotic Plaque: Model Selection, Reproducibility, and Validation.
Purpose: compare four known pharmacokinetic models for their ability to describe dynamic contrast material-enhanced magnetic resonance (MR) imaging of carotid atherosclerotic plaques, to determine reproducibility, and to validate the results with histologic findings. Materials and Methods: The study was approved by the institutional medical ethics committee. Written informed consent was obtained from all patients. Forty-five patients with 30%-99% carotid stenosis underwent dynamic contrast-enhanced MR imaging. Plaque enhancement was measured at 16 time points at approximately 25-second image intervals by using a gadolinium-based contrast material. Pharmacokinetic parameters (volume transfer constant, Ktrans; extracellular extravascular volume fraction, v e; and blood plasma fraction, v p) were determined by fitting a two-compartment model to plaque and blood gadolinium concentration curves. The relative fit errors and parameter uncertainties were determined to find the most suitable model. Sixteen patients underwent imaging twice to determine reproducibility. Carotid endarterectomy specimens from 16 patients who were scheduled for surgery were collected for histologic validation. Parameter uncertainties were compared with the Wilcoxon signed rank test. Reproducibility was assessed by using the coefficient of variation. Correlation with histologic findings was evaluated with the Pearson correlation coefficient. Results: The mean relative fit uncertainty (+/- standard error) for Ktrans was 10% +/- 1 with the Patlak model, which was significantly lower than that with the Tofts (20% +/- 1), extended Tofts (33% +/- 3), and extended graphical (29% +/- 3) models (P <.001). The relative uncertainty for v p was 20% 6 2 with the Patlak model and was significantly higher with the extended Tofts (46% +/- 9) and extended graphical (35% +/- 5) models (P <.001). The reproducibility (coefficient of variation) for the Patlak model was 16% for Ktrans and 26% for v p. Significant positive correlations were found between Ktrans and the endothelial microvessel content determined on histologic slices (Pearson r = 0.72, P = .005). Conclusion: The Patlak model is most suited for describing carotid plaque enhancement. Correlation with histologic findings validated Ktrans as an indicator of plaque microvasculature, and the reproducibility of Ktrans was good. (C)RSNA, 201
Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model
In this manuscript a unified framework for conducting inference on complex
aggregated data in high dimensional settings is proposed. The data are assumed
to be a collection of multiple non-Gaussian realizations with underlying
undirected graphical structures. Utilizing the concept of median graphs in
summarizing the commonality across these graphical structures, a novel
semiparametric approach to modeling such complex aggregated data is provided
along with robust estimation of the median graph, which is assumed to be
sparse. The estimator is proved to be consistent in graph recovery and an upper
bound on the rate of convergence is given. Experiments on both synthetic and
real datasets are conducted to illustrate the empirical usefulness of the
proposed models and methods
Neural Connectivity with Hidden Gaussian Graphical State-Model
The noninvasive procedures for neural connectivity are under questioning.
Theoretical models sustain that the electromagnetic field registered at
external sensors is elicited by currents at neural space. Nevertheless, what we
observe at the sensor space is a superposition of projected fields, from the
whole gray-matter. This is the reason for a major pitfall of noninvasive
Electrophysiology methods: distorted reconstruction of neural activity and its
connectivity or leakage. It has been proven that current methods produce
incorrect connectomes. Somewhat related to the incorrect connectivity
modelling, they disregard either Systems Theory and Bayesian Information
Theory. We introduce a new formalism that attains for it, Hidden Gaussian
Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden
by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS
is equivalent to a frequency domain Linear State Space Model (LSSM) but with
sparse connectivity prior. The mathematical contribution here is the theory for
high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS
can attenuate the leakage effect in the most critical case: the distortion EEG
signal due to head volume conduction heterogeneities. Its application in EEG is
illustrated with retrieved connectivity patterns from human Steady State Visual
Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence
for noninvasive procedures of neural connectivity: concurrent EEG and
Electrocorticography (ECoG) recordings on monkey. Open source packages are
freely available online, to reproduce the results presented in this paper and
to analyze external MEEG databases
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Caring, sharing widgets: a toolkit of sensitive widgets
Although most of us communicate using multiple sensory modalities in our lives, and many of our computers are similarly capable of multi-modal interaction, most human-computer interaction is predominantly in the visual mode. This paper describes a toolkit of widgets that are capable of presenting themselves in multiple modalities, but further are capapble of adapting their presentation to suit the contexts and environments in which they are used. This is of increasing importance as the use of mobile devices becomes ubiquitous
The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search
This article presents a new search algorithm for the NP-hard problem of
optimizing functions of binary variables that decompose according to a
graphical model. It can be applied to models of any order and structure. The
main novelty is a technique to constrain the search space based on the topology
of the model. When pursued to the full search depth, the algorithm is
guaranteed to converge to a global optimum, passing through a series of
monotonously improving local optima that are guaranteed to be optimal within a
given and increasing Hamming distance. For a search depth of 1, it specializes
to Iterated Conditional Modes. Between these extremes, a useful tradeoff
between approximation quality and runtime is established. Experiments on models
derived from both illustrative and real problems show that approximations found
with limited search depth match or improve those obtained by state-of-the-art
methods based on message passing and linear programming.Comment: C++ Source Code available from
http://hci.iwr.uni-heidelberg.de/software.ph
Geometry of rank tests
We study partitions of the symmetric group which have desirable geometric
properties. The statistical tests defined by such partitions involve counting
all permutations in the equivalence classes. These permutations are the linear
extensions of partially ordered sets specified by the data. Our methods refine
rank tests of non-parametric statistics, such as the sign test and the runs
test, and are useful for the exploratory analysis of ordinal data. Convex rank
tests correspond to probabilistic conditional independence structures known as
semi-graphoids. Submodular rank tests are classified by the faces of the cone
of submodular functions, or by Minkowski summands of the permutohedron. We
enumerate all small instances of such rank tests. Graphical tests correspond to
both graphical models and to graph associahedra, and they have excellent
statistical and algorithmic properties.Comment: 8 pages, 4 figures. See also http://bio.math.berkeley.edu/ranktests/.
v2: Expanded proofs, revised after reviewer comment
Factorial graphical lasso for dynamic networks
Dynamic networks models describe a growing number of important scientific
processes, from cell biology and epidemiology to sociology and finance. There
are many aspects of dynamical networks that require statistical considerations.
In this paper we focus on determining network structure. Estimating dynamic
networks is a difficult task since the number of components involved in the
system is very large. As a result, the number of parameters to be estimated is
bigger than the number of observations. However, a characteristic of many
networks is that they are sparse. For example, the molecular structure of genes
make interactions with other components a highly-structured and therefore
sparse process.
Penalized Gaussian graphical models have been used to estimate sparse
networks. However, the literature has focussed on static networks, which lack
specific temporal constraints. We propose a structured Gaussian dynamical
graphical model, where structures can consist of specific time dynamics, known
presence or absence of links and block equality constraints on the parameters.
Thus, the number of parameters to be estimated is reduced and accuracy of the
estimates, including the identification of the network, can be tuned up. Here,
we show that the constrained optimization problem can be solved by taking
advantage of an efficient solver, logdetPPA, developed in convex optimization.
Moreover, model selection methods for checking the sensitivity of the inferred
networks are described. Finally, synthetic and real data illustrate the
proposed methodologies.Comment: 30 pp, 5 figure
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