Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas

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. [Display omitted] ► We propose an automatic and robust method for fiber bundle segmentation in massive tractography datasets. ► The method is based on a novel HARDI multi-subject human brain fiber bundle atlas, composed of 36 known deep white matter bundles. ► The atlas also contains 47 superficial white matter bundles in each hemisphere, included in a multisubject bundle atlas for the first time. ► The method considers the fiber shape, position and length information in the segmentation, leading to better results than ROI-based approaches. ► Results can be used for population studies where each generic bundle is analyzed separately.