398 research outputs found
Prior-based Coregistration and Cosegmentation
We propose a modular and scalable framework for dense coregistration and
cosegmentation with two key characteristics: first, we substitute ground truth
data with the semantic map output of a classifier; second, we combine this
output with population deformable registration to improve both alignment and
segmentation. Our approach deforms all volumes towards consensus, taking into
account image similarities and label consistency. Our pipeline can incorporate
any classifier and similarity metric. Results on two datasets, containing
annotations of challenging brain structures, demonstrate the potential of our
method.Comment: The first two authors contributed equall
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Sawtooth period changes with mode conversion current drive on Alcator C-Mod
DEFC0299ER54512. Reproduction, translation, publication, use and disposal, in whole or in part, by or for the United States government is permitted. Submitted for publication to Plasma Physics and Controlled Fusion. Sawtooth period changes with mode conversion current drive on Alcator C-Mo
OntoGene in BioCreative II
BACKGROUND: Research scientists and companies working in the domains of biomedicine and genomics are increasingly faced with the problem of efficiently locating, within the vast body of published scientific findings, the critical pieces of information that are needed to direct current and future research investment. RESULTS: In this report we describe approaches taken within the scope of the second BioCreative competition in order to solve two aspects of this problem: detection of novel protein interactions reported in scientific articles, and detection of the experimental method that was used to confirm the interaction. Our approach to the former problem is based on a high-recall protein annotation step, followed by two strict disambiguation steps. The remaining proteins are then combined according to a number of lexico-syntactic filters, which deliver high-precision results while maintaining reasonable recall. The detection of the experimental methods is tackled by a pattern matching approach, which has delivered the best results in the official BioCreative evaluation. CONCLUSION: Although the results of BioCreative clearly show that no tool is sufficiently reliable for fully automated annotations, a few of the proposed approaches (including our own) already perform at a competitive level. This makes them interesting either as standalone tools for preliminary document inspection, or as modules within an environment aimed at supporting the process of curation of biomedical literature
Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
There is a rising need for computational models that can complementarily
leverage data of different modalities while investigating associations between
subjects for population-based disease analysis. Despite the success of
convolutional neural networks in representation learning for imaging data, it
is still a very challenging task. In this paper, we propose a generalizable
framework that can automatically integrate imaging data with non-imaging data
in populations for uncertainty-aware disease prediction. At its core is a
learnable adaptive population graph with variational edges, which we
mathematically prove that it is optimizable in conjunction with graph
convolutional neural networks. To estimate the predictive uncertainty related
to the graph topology, we propose the novel concept of Monte-Carlo edge
dropout. Experimental results on four databases show that our method can
consistently and significantly improve the diagnostic accuracy for Autism
spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its
generalizability in leveraging multimodal data for computer-aided diagnosis.Comment: Accepted to MICCAI 202
GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis
Unsupervised learning can discover various unseen diseases, relying on
large-scale unannotated medical images of healthy subjects. Towards this,
unsupervised methods reconstruct a single medical image to detect outliers
either in the learned feature space or from high reconstruction loss. However,
without considering continuity between multiple adjacent slices, they cannot
directly discriminate diseases composed of the accumulation of subtle
anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has
shown how unsupervised anomaly detection is associated with disease stages.
Therefore, we propose a two-step method using Generative Adversarial
Network-based multiple adjacent brain MRI slice reconstruction to detect AD at
various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1
loss---trained on 3 healthy slices to reconstruct the next 3
ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss
(e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground
truth images. The results show that we can reliably detect AD at a very early
stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late
stage much more accurately with AUC 0.917; since our method is fully
unsupervised, it should also discover and alert any anomalies including rare
disease.Comment: 10 pages, 4 figures, Accepted to Lecture Notes in Bioinformatics
(LNBI) as a volume in the Springer serie
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
We propose a mesh-based technique to aid in the classification of Alzheimer's
disease dementia (ADD) using mesh representations of the cortex and subcortical
structures. Deep learning methods for classification tasks that utilize
structural neuroimaging often require extensive learning parameters to
optimize. Frequently, these approaches for automated medical diagnosis also
lack visual interpretability for areas in the brain involved in making a
diagnosis. This work: (a) analyzes brain shape using surface information of the
cortex and subcortical structures, (b) proposes a residual learning framework
for state-of-the-art graph convolutional networks which offer a significant
reduction in learnable parameters, and (c) offers visual interpretability of
the network via class-specific gradient information that localizes important
regions of interest in our inputs. With our proposed method leveraging the use
of cortical and subcortical surface information, we outperform other machine
learning methods with a 96.35% testing accuracy for the ADD vs. healthy control
problem. We confirm the validity of our model by observing its performance in a
25-trial Monte Carlo cross-validation. The generated visualization maps in our
study show correspondences with current knowledge regarding the structural
localization of pathological changes in the brain associated to dementia of the
Alzheimer's type.Comment: Accepted for the Shape in Medical Imaging (ShapeMI) workshop at
MICCAI International Conference 202
SPHERE: the exoplanet imager for the Very Large Telescope
Observations of circumstellar environments to look for the direct signal of
exoplanets and the scattered light from disks has significant instrumental
implications. In the past 15 years, major developments in adaptive optics,
coronagraphy, optical manufacturing, wavefront sensing and data processing,
together with a consistent global system analysis have enabled a new generation
of high-contrast imagers and spectrographs on large ground-based telescopes
with much better performance. One of the most productive is the
Spectro-Polarimetic High contrast imager for Exoplanets REsearch (SPHERE)
designed and built for the ESO Very Large Telescope (VLT) in Chile. SPHERE
includes an extreme adaptive optics system, a highly stable common path
interface, several types of coronagraphs and three science instruments. Two of
them, the Integral Field Spectrograph (IFS) and the Infra-Red Dual-band Imager
and Spectrograph (IRDIS), are designed to efficiently cover the near-infrared
(NIR) range in a single observation for efficient young planet search. The
third one, ZIMPOL, is designed for visible (VIR) polarimetric observation to
look for the reflected light of exoplanets and the light scattered by debris
disks. This suite of three science instruments enables to study circumstellar
environments at unprecedented angular resolution both in the visible and the
near-infrared. In this work, we present the complete instrument and its on-sky
performance after 4 years of operations at the VLT.Comment: Final version accepted for publication in A&
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Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties
The main objective of our work is to perform an in depth analysis of the structural features of normal choriocapillaris imaged with OCT Angiography. Specifically, we provide an optimal radius for a circular Region of Interest (ROI) to obtain a stable estimate of the subfoveal choriocapillaris density and characterize its textural properties using Markov Random Fields. On each binarized image of the choriocapillaris OCT Angiography we performed simulated measurements of the subfoveal choriocapillaris densities with circular Regions of Interest (ROIs) of different radii and with small random displacements from the center of the Foveal Avascular Zone (FAZ). We then calculated the variability of the density measure with different ROI radii. We then characterized the textural features of choriocapillaris binary images by estimating the parameters of an Ising model. For each image we calculated the Optimal Radius (OR) as the minimum ROI radius required to obtain a standard deviation in the simulation below 0.01. The density measured with the individual OR was 0.52 ± 0.07 (mean ± STD). Similar density values (0.51 ± 0.07) were obtained using a fixed ROI radius of 450 μm. The Ising model yielded two parameter estimates (β = 0.34 ± 0.03; γ = 0.003 ± 0.012; mean ± STD), characterizing pixel clustering and white pixel density respectively. Using the estimated parameters to synthetize new random textures via simulation we obtained a good reproduction of the original choriocapillaris structural features and density. In conclusion, we developed an extensive characterization of the normal subfoveal choriocapillaris that might be used for flow analysis and applied to the investigation pathological alterations
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