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 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.
Pamela Guevara Cyril Poupon Denis Riviere Yann Cointepas Linda Marrakchi Maxime Descoteaux Pierre Fillard Bertrand Thirion Jean-Francois Mangin
Neurospin, CEA, Gif-sur-Yvette, FranceInstitut F′ed′eratif de Recherche 49, Gif-sur-Yvette, France Universit′e de Sherbrooke, Sherbrooke, Canada Neurospin, CEA, Gif-sur-Yvette, FranceInstitut F′ed′eratif de Recherche 49, Gif-sur-Yvette, FrancePa Neurospin, CEA, Gif-sur-Yvette, France Institut Federatif de Recherche 49, Gif-sur-Yvette, France
国际会议
北京
英文
550-557
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）