511 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
Mise en évidence d’une période de 2-3 ans dans l’évolution de la plage du Truc Vert (Gironde)
Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex
The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously
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
Analyse de l’évolution morphodynamique de la plage du Truc Vert (Gironde) sur une période décennale
Latent Patient Network Learning for Automatic Diagnosis
Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful
machine learning tool for Computer Aided Diagnosis (CADx) and disease
prediction. A key component in these models is to build a population graph,
where the graph adjacency matrix represents pair-wise patient similarities.
Until now, the similarity metrics have been defined manually, usually based on
meta-features like demographics or clinical scores. The definition of the
metric, however, needs careful tuning, as GCNs are very sensitive to the graph
structure. In this paper, we demonstrate for the first time in the CADx domain
that it is possible to learn a single, optimal graph towards the GCN's
downstream task of disease classification. To this end, we propose a novel,
end-to-end trainable graph learning architecture for dynamic and localized
graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is
spatial and inductive, and can thus infer previously unseen patients as well.
We demonstrate significant classification improvements with our learned graph
on two CADx problems in medicine. We further explain and visualize this result
using an artificial dataset, underlining the importance of graph learning for
more accurate and robust inference with GCNs in medical applications
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
Decision Support for Intoxication Prediction Using Graph Convolutional Networks
Every day, poison control centers (PCC) are called for immediate
classification and treatment recommendations if an acute intoxication is
suspected. Due to the time-sensitive nature of these cases, doctors are
required to propose a correct diagnosis and intervention within a minimal time
frame. Usually the toxin is known and recommendations can be made accordingly.
However, in challenging cases only symptoms are mentioned and doctors have to
rely on their clinical experience. Medical experts and our analyses of a
regional dataset of intoxication records provide evidence that this is
challenging, since occurring symptoms may not always match the textbook
description due to regional distinctions, inter-rater variance, and
institutional workflow. Computer-aided diagnosis (CADx) can provide decision
support, but approaches so far do not consider additional information of the
reported cases like age or gender, despite their potential value towards a
correct diagnosis. In this work, we propose a new machine learning based CADx
method which fuses symptoms and meta information of the patients using graph
convolutional networks. We further propose a novel symptom matching method that
allows the effective incorporation of prior knowledge into the learning process
and evidently stabilizes the poison prediction. We validate our method against
10 medical doctors with different experience diagnosing intoxication cases for
10 different toxins from the PCC in Munich and show our method's superiority in
performance for poison prediction.Comment: 10 pages, 3 figure
Observation and Modeling of Ion Cyclotron Range of Frequencies Waves in the Mode Conversion Region of Alcator C-Mod
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