Inference of a HARDI fiber bundle atlas using a two-level clustering strategy

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 cluster...

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Bibliographic Details
Published inMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 13; no. Pt 1; p. 550
Main Authors Guevara, Pamela, Poupon, Cyril, Rivière, Denis, Cointepas, Yann, Marrakchi, Linda, Descoteaux, Maxime, Fillard, Pierre, Thirion, Bertrand, Mangin, Jean-François
Format Journal Article
LanguageEnglish
Published Germany 2010
Subjects
Algorithms
Brain - anatomy & histology
Computer Simulation
Diffusion Magnetic Resonance Imaging - methods
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Models, Anatomic
Models, Neurological
Nerve Fibers, Myelinated - ultrastructure
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
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Summary: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 intersubject 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.