6,965 research outputs found
Material parameter estimation and hypothesis testing on a 1D viscoelastic stenosis model: Methodology
This is the post-print version of the final published paper that is available from the link below. Copyright @ 2013 Walter de Gruyter GmbH.Non-invasive detection, localization and characterization of an arterial stenosis (a blockage or partial blockage in the artery) continues to be an important problem in medicine. Partial blockage stenoses are known to generate disturbances in blood flow which generate shear waves in the chest cavity. We examine a one-dimensional viscoelastic model that incorporates Kelvin–Voigt damping and internal variables, and develop a proof-of-concept methodology using simulated data. We first develop an estimation procedure for the material parameters. We use this procedure to determine confidence intervals for the estimated parameters, which indicates the efficacy of finding parameter estimates in practice. Confidence intervals are computed using asymptotic error theory as well as bootstrapping. We then develop a model comparison test to be used in determining if a particular data set came from a low input amplitude or a high input amplitude; this we anticipate will aid in determining when stenosis is present. These two thrusts together will serve as the methodological basis for our continuing analysis using experimental data currently being collected.National Institute of Allergy and Infectious Diseases, Air Force Office of Scientific Research, Department of Education, and Engineering and Physical Sciences Research Council
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
Quality control (QC) of medical images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visually at significant time and operator cost. We aim to automate the process by formulating a probabilistic network that estimates uncertainty through a heteroscedastic noise model, hence providing a proxy measure of task-specific image quality that is learnt directly from the data. By augmenting the training data with different types of simulated k-space artefacts, we propose a novel cascading CNN architecture based on a student-teacher framework to decouple sources of uncertainty related to different k-space augmentations in an entirely self-supervised manner. This enables us to predict separate uncertainty quantities for the different types of data degradation. While the uncertainty measures reflect the presence and severity of image artefacts, the network also provides the segmentation predictions given the quality of the data. We show models trained with simulated artefacts provide informative measures of uncertainty on real-world images and we validate our uncertainty predictions on problematic images identified by human-raters
High-order space-time finite element schemes for acoustic and viscodynamic wave equations with temporal decoupling
Copyright @ 2014 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited.We revisit a method originally introduced by Werder et al. (in Comput. Methods Appl. Mech. Engrg., 190:6685–6708, 2001) for temporally discontinuous Galerkin FEMs applied to a parabolic partial differential equation. In that approach, block systems arise because of the coupling of the spatial systems through inner products of the temporal basis functions. If the spatial finite element space is of dimension D and polynomials of degree r are used in time, the block system has dimension (r + 1)D and is usually regarded as being too large when r > 1. Werder et al. found that the space-time coupling matrices are diagonalizable over inline image for r ⩽100, and this means that the time-coupled computations within a time step can actually be decoupled. By using either continuous Galerkin or spectral element methods in space, we apply this DG-in-time methodology, for the first time, to second-order wave equations including elastodynamics with and without Kelvin–Voigt and Maxwell–Zener viscoelasticity. An example set of numerical results is given to demonstrate the favourable effect on error and computational work of the moderately high-order (up to degree 7) temporal and spatio-temporal approximations, and we also touch on an application of this method to an ambitious problem related to the diagnosis of coronary artery disease
Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks
We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
two-stages deep convolutional neural networks (DCNN). Compared to existing
DCNN-based DR detection methods, the proposed algorithm have the following
advantages: (1) Our method can point out the location and type of lesions in
the fundus images, as well as giving the severity grades of DR. Moreover, since
retina lesions and DR severity appear with different scales in fundus images,
the integration of both local and global networks learn more complete and
specific features for DR analysis. (2) By introducing imbalanced weighting map,
more attentions will be given to lesion patches for DR grading, which
significantly improve the performance of the proposed algorithm. In this study,
we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
images from Kaggle competition dataset. Under the guidance of clinical
ophthalmologists, the experimental results show that our local lesion detection
net achieve comparable performance with trained human observers, and the
proposed imbalanced weighted scheme also be proved to significantly improve the
capability of our DCNN-based DR grading algorithm
A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal
Patient movement during the acquisition of magnetic resonance images (MRI) can cause unwanted image artefacts. These artefacts may affect the quality of clinical diagnosis
and cause errors in automated image analysis. In this work,
we present a method for generating realistic motion artefacts
from artefact-free magnitude MRI data to be used in deep
learning frameworks, increasing training appearance variability and ultimately making machine learning algorithms such
as convolutional neural networks (CNNs) more robust to the
presence of motion artefacts. By modelling patient movement as
a sequence of randomly-generated, ‘demeaned’, rigid 3D affine
transforms, we resample artefact-free volumes and combine these
in k-space to generate motion artefact data. We show that
by augmenting the training of semantic segmentation CNNs
with artefacts, we can train models that generalise better and
perform more reliably in the presence of artefact data, with
negligible cost to their performance on clean data. We show
that the performance of models trained using artefact data on
segmentation tasks on real-world test-retest image pairs is more
robust. We also demonstrate that our augmentation model can
be used to learn to retrospectively remove certain types of motion
artefacts from real MRI scans. Finally, we show that measures
of uncertainty obtained from motion augmented CNN models
reflect the presence of artefacts and can thus provide relevant
information to ensure the safe usage of deep learning extracted
biomarkers in a clinical pipeline
Histological and cytological imaging using Fourier ptychographic microscopy
Structural imaging using light microscopy is a cornerstone of histology and cytology. However, the utility of the optical microscope for diagnostic imaging is limited by the fundamental tradeoff between the field of view and spatial resolution and a reliance on exogenous dyes to generate sufficient image contrast. Fourier Ptychographic Microscopy (FPM) is a complex imaging modality with the potential to overcome these limitations by recovering high-resolution images of sample amplitude and phase from a set of low-resolution raw images captured under inclined illumination. In this article we explore the application of FPM to clinical imaging using a simple, low-cost FPM system and simulated and experimental data to explore the influence of both image acquisition parameters and hardware configuration on image quality and imaging throughput. The practical performance of the method is investigated by imaging peripheral blood films and histological tissue sections. We find that, at the cost of increased computational complexity, FPM increases the information capture capacity of the optical microscope significantly, allowing label-free examination and quantification of features such as tissue and cell morphology over large sample areas
Area and individual differences in personal crime victimization incidence: The role of individual, lifestyle/routine activities and contextual predictors
This article examines how personal crime differences between areas and between individuals are predicted by area and population heterogeneity and their synergies. It draws on lifestyle/routine activities and social disorganization theories to model the number of personal victimization incidents over individuals including routine activities and area characteristics, respectively, as well as their (cross-cluster) interactions. The methodology employs multilevel or hierarchical negative binomial regression with extra binomial variation using data from the British Crime Survey and the UK Census. Personal crime rates differ substantially across areas, reflecting to a large degree the clustering of individuals with measured vulnerability factors in the same areas. Most factors suggested by theory and previous research are conducive to frequent personal victimization except the following new results. Pensioners living alone in densely populated areas face disproportionally high numbers of personal crimes. Frequent club and pub visits are associated with more personal crimes only for males and adults living with young children, respectively. Ethnic minority individuals experience fewer personal crimes than whites. The findings suggest integrating social disorganization and lifestyle theories and prioritizing resources to the most vulnerable, rather than all, residents of poor and densely populated areas to prevent personal crimes
Maze solvers demystified and some other thoughts
There is a growing interest towards implementation of maze solving in
spatially-extended physical, chemical and living systems. Several reports of
prototypes attracted great publicity, e.g. maze solving with slime mould and
epithelial cells, maze navigating droplets. We show that most prototypes
utilise one of two phenomena: a shortest path in a maze is a path of the least
resistance for fluid and current flow, and a shortest path is a path of the
steepest gradient of chemoattractants. We discuss that substrates with
so-called maze-solving capabilities simply trace flow currents or chemical
diffusion gradients. We illustrate our thoughts with a model of flow and
experiments with slime mould. The chapter ends with a discussion of experiments
on maze solving with plant roots and leeches which show limitations of the
chemical diffusion maze-solving approach.Comment: This is a preliminary version of the chapter to be published in
Adamatzky A. (Ed.) Shortest path solvers. From software to wetware. Springer,
201
Infection of a yellow baboon with simian immunodeficiency virus from African green monkeys:evidence for cross-species transmission in the wild
Many African primates are known to be naturally infected with simian immunodeficiency viruses (SIVs), but only a fraction of these viruses has been molecularly characterized. One primate species for which only serological evidence of SIV infection has been reported is the yellow baboon (Papio hamadryas cynocephalus). Two wild-living baboons with strong SIVAGM seroreactivity were previously identified in a Tanzanian national park where baboons and African green monkeys shared the same habitat (T. Kodama, D. P. Silva, M. D. Daniel, J. E. Phillips-Conroy, C. J. Jolly, J. Rogers, and R. C. Desrosiers, AIDS Res. Hum. Retroviruses 5:337-343, 1989). To determine the genetic identity of the viruses infecting these animals, we used PCR to examine SIV sequences directly in uncultured leukocyte DNA. Targeting two different, nonoverlapping genomic regions, we amplified and sequenced a 673-bp gag gene fragment and a 908-bp env gene fragment from one of the two baboons. Phylo-genetic analyses revealed that this baboon was infected with an SIVAGM strain of the vervet subtype. These results provide the first direct evidence for simian-to-simian cross-species transmission of SIV in the wild
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