Institute Federatif Of Recherche 49

Gif-sur-Yvette, France

Institute Federatif Of Recherche 49

Gif-sur-Yvette, France
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Yoo S.W.,Korea University | Yoo S.W.,KAIST | Yoo S.W.,Samsung | Guevara P.,CEA Saclay Nuclear Research Center | And 9 more authors.
PLoS ONE | Year: 2015

We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively. Copyright © 2015 Yoo et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Guevara P.,CEA Saclay Nuclear Research Center | Guevara P.,Institute Federatif Of Recherche 49 | Guevara P.,University of Concepción | Poupon C.,CEA Saclay Nuclear Research Center | And 11 more authors.
NeuroImage | Year: 2011

This paper presents a clustering method that detects the fiber bundles embedded in any MR-diffusion based tractography dataset. Our method can be seen as a compressing operation, capturing the most meaningful information enclosed in the fiber dataset. For the sake of efficiency, part of the analysis is based on clustering the white matter (WM) voxels rather than the fibers. The resulting regions of interest are used to define subset of fibers that are subdivided further into consistent bundles using a clustering of the fiber extremities. The dataset is reduced from more than one million fiber tracts to about two thousand fiber bundles. Validations are provided using simulated data and a physical phantom. We see our approach as a crucial preprocessing step before further analysis of huge fiber datasets. An important application will be the inference of detailed models of the subdivisions of white matter pathways and the mapping of the main U-fiber bundles. © 2010 Elsevier Inc.


Guevara P.,CEA Saclay Nuclear Research Center | Guevara P.,Institute Federatif Of Recherche 49 | Guevara P.,University of Concepción | Duclap D.,CEA Saclay Nuclear Research Center | And 16 more authors.
NeuroImage | Year: 2012

This paper presents a method for automatic segmentation of white matter fiber bundles from massive dMRI tractography datasets. The method is based on a multi-subject bundle atlas derived from a two-level intra-subject and inter-subject clustering strategy. This atlas is a model of the brain white matter organization, computed for a group of subjects, made up of a set of generic fiber bundles that can be detected in most of the population. Each atlas bundle corresponds to several inter-subject clusters manually labeled to account for subdivisions of the underlying pathways often presenting large variability across subjects. An atlas bundle is represented by the multi-subject list of the centroids of all intra-subject clusters in order to get a good sampling of the shape and localization variability. The atlas, composed of 36 known deep white matter bundles and 47 superficial white matter bundles in each hemisphere, was inferred from a first database of 12 brains. It was successfully used to segment the deep white matter bundles in a second database of 20 brains and most of the superficial white matter bundles in 10 subjects of the same database. © 2012 Elsevier Inc.


Guevara P.,CEA Saclay Nuclear Research Center | Guevara P.,Institute Federatif Of Recherche 49 | Guevara P.,University of Concepción | Duclap D.,CEA Saclay Nuclear Research Center | And 11 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

This paper presents a method for automatic segmentation of some short association fiber bundles from massive dMRI tractography datasets. The method is based on a multi-subject bundle atlas derived from a two-level intra-subject and inter-subject clustering strategy. Each atlas bundle corresponds to one or more inter-subject clusters, presenting similar shapes. An atlas bundle is represented by the multi-subject list of the centroids of all intra-subject clusters in order to get a good sampling of the shape and localization variability. An atlas of 47 bundles is inferred from a first database of 12 brains, and used to segment the same bundles in a second database of 10 brains. © 2011 Springer-Verlag.


Marrakchi-Kacem L.,CEA Saclay Nuclear Research Center | Marrakchi-Kacem L.,Institute Federatif Of Recherche 49 | Delmaire C.,Institute Federatif Of Recherche 49 | Delmaire C.,Pitie Salpetriere Hospital | And 17 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

The deep brain nuclei play an important role in many brain functions and particularly motor control. Damage to these structures result in movement disorders such as in Parkinson's disease or Huntington's disease, or behavioural disorders such as Tourette syndrome. In this paper, we propose to study the connectivity profile of the deep nuclei to the motor, associative or limbic areas and we introduce a novel tool to build a probabilistic atlas of these connections to the cortex directly on the surface of the cortical mantel, as it corresponds to the space of functional interest. The tool is then applied on two populations of healthy volunteers and patients suffering from severe Huntington's disease to produce two surface atlases of the connectivity of the basal ganglia to the cortical areas. Finally, robust statistics are used to characterize the differences of that connectivity between the two populations, providing new connectivity-based biomarkers of the pathology. © 2010 Springer-Verlag.


Guevara P.,CEA Saclay Nuclear Research Center | Guevara P.,Institute Federatif Of Recherche 49 | Poupon C.,CEA Saclay Nuclear Research Center | Poupon C.,Institute Federatif Of Recherche 49 | And 15 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

This paper presents a method inferring a model of the brain white matter organisation from HARDI tractography results computed for a group of subjects. This model is made up of a set of generic fiber bundles that can be detected in most of the population. Our approach is based on a two-level clustering strategy. The first level is a multiresolution intra-subject clustering of the million tracts that are computed for each brain. This analysis reduces the complexity of the data to a few thousands fiber bundles for each subject. The second level is an inter-subject clustering over fiber bundle centroids from all the subjects using a pairwise distance computed after spatial normalization. The resulting model includes the large bundles of anatomical literature and about 20 U-fiber bundles in each hemisphere. © 2010 Springer-Verlag.


Roca P.,CEA Saclay Nuclear Research Center | Roca P.,Institute Federatif Of Recherche 49 | Tucholka A.,CEA Saclay Nuclear Research Center | Tucholka A.,Institute Federatif Of Recherche 49 | And 9 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

This paper presents a connectivity-based parcellation of the human post-central gyrus, at the level of the group of subjects. The dimension of the clustering problem is reduced using a set of cortical regions of interest determined at the inter-subject level using a surface-based coordinate system, and representing the regions with a strong connection to the post-central gyrus. This process allows a clustering based on criteria which are more reproducible across subjects than in an intra-subject approach. We obtained parcels relatively stable in localisation across subjects as well as homogenous and well-separated to each other in terms of connectivity profiles. To address the parcellation at the inter-subject level provides a direct matching between parcels across subjects. In addition, this method allows the identification of subject-specific parcels. This property could be useful for the study of pathologies. © 2010 Springer-Verlag.

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